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Web Age Information Management 17th International Conference WAIM 2016 Nanchang China June 3 5 2016 Proceedings Part I 1st Edition Bin Cui

Web Age Information Management 17th International Conference WAIM 2016 Nanchang China June 3 5 2016 Proceedings Part I 1st Edition Bin Cui Web Age Information Management 17th International Conference WAIM 2016 Nanchang China June 3 5 2016 Proceedings Part I 1st Edition Bin Cui Web Age Information Management 17th International Conference WAIM 2016 Nanchang China June 3 5 2016 Proceedings Part I 1st Edition Bin Cui

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Bin Cui · Nan Zhang · Jianliang Xu Xiang Lian · Dexi Liu (Eds.) 123 LNCS 9658 17th International Conference, WAIM 2016 Nanchang, China, June 3–5, 2016 Proceedings, Part I Web-Age Information Management
Lecture Notes in Computer Science 9658 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zürich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/7409
Bin Cui • Nan Zhang • Jianliang Xu Xiang Lian • Dexi Liu (Eds.) Web-Age Information Management 17th International Conference, WAIM 2016 Nanchang, China, June 3–5, 2016 Proceedings, Part I 123
Editors Bin Cui Peking University Beijing China Nan Zhang The George Washington University Washington, D.C. USA Jianliang Xu Hong Kong Baptist University Kowloon Tong, Hong Kong SAR China Xiang Lian University of Texas Rio Grande Valley Edinburg, TX USA Dexi Liu Jiangxi University of Finance and Economics Nanchang China ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-319-39936-2 ISBN 978-3-319-39937-9 (eBook) DOI 10.1007/978-3-319-39937-9 Library of Congress Control Number: 2016940123 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Preface This volume contains the proceedings of the 17th International Conference on Web- Age Information Management (WAIM), held during June 3–5, 2016, in Nanchang, Jiangxi, China. As a flagship conference in the Asia-Pacific region focusing on the research, development, and applications of Web information management, its success has been witnessed through the previous conference series that were held in Shanghai (2000), Xi’an (2001), Beijing (2002), Chengdu (2003), Dalian (2004), Hangzhou (2005), Hong Kong (2006), Huangshan (2007), Zhangjiajie (2008), Suzhou (2009), Jiuzhaigou (2010), Wuhan (2011), Harbin (2012), Beidahe (2013), Macau (2014), and Qingdao (2015). With the fast development of Web-related technologies, we expect that WAIM will become an increasingly popular forum to bring together outstanding researchers in this field from all over the world. This high-quality program would not have been possible without the authors who chose WAIM for disseminating their contributions. Out of 249 submissions to the research track and 17 to the demonstration track, the conference accepted 80 research papers and eight demonstrations. The contributed papers address a wide range of topics, such as big data analytics, data mining, query processing and optimization, security, privacy, trust, recommender systems, spatial databases, information retrieval and Web search, information extraction and integration, data and information quality, distributed and cloud computing, among others. The technical program of WAIM 2016 also included two keynote talks by Profs. Beng Chin Ooi (National University of Singapore) and Yanchun Zhang (Victoria University, Australia), as well as three talks in the Distinguished Young Lecturer Series by Profs. Tingjian Ge (University of Massachusetts at Lowell), Hua Lu (Aalborg University), and Haibo Hu (Hong Kong Polytechnic University). We are immensely grateful to these distinguished guests for their invaluable contributions to the confer- ence program. A conference like WAIM can only succeed as a team effort. We are deeply thankful to the Program Committee members and the reviewers for their invaluable efforts. Special thanks to the local Organizing Committee headed by Guoqiong Liao and Xiaobing Mao. Many thanks also go to our workshop co-chairs (Shaoxu Song and Yongxin Tong), proceedings co-chairs (Xiang Lian and Dexi Liu), DYL co-chairs (Hong Gao and Weiyi Meng), demo co-chairs (Xiping Liu and Yi Yu), publicity co- chairs (Ye Yuan, Hua Lu, and Chengkai Li), registration chair (Yong Yang), and finance chair (Bo Shen). Last but not least, we wish to express our gratitude for the hard
work of our webmaster (Bo Yang), and for our sponsors who generously supported the smooth running of our conference. We hope you enjoy the proceedings WAIM 2016! June 2016 Zhanhuai Li Sang Kyun Cha Changxuan Wan Bin Cui Nan Zhang Jianliang Xu VI Preface
Organization Organizing Committee Honor Chair Qiao Wang Jiangxi University of Finance and Economics, China General Co-chairs Zhanhuai Li Northwestern Polytechnical University, China Sang Kyun Cha Seoul National University, Korea Changxuan Wan Jiangxi University of Finance and Economics, China PC Co-chairs Bin Cui Peking University, China Nan Zhang George Washington University, USA Jianliang Xu Hong Kong Baptist University, SAR China Workshop Co-chairs Shaoxu Song Tsinghua University, China Yongxin Tong Beihang University, China Proceedings Co-chairs Xiang Lian The University of Texas Rio Grande Valley, USA Dexi Liu Jiangxi University of Finance and Economics, China DYL Series Co-chairs (Distinguished Young Lecturer) Hong Gao Harbin Institute of Technology, China Weiyi Meng SUNY Binghamton, USA Demo Co-chairs Xiping Liu Jiangxi University of Finance and Economics, China Yi Yu National Institute of Informatics, Japan
Publicity Co-chairs Ye Yuan Northeastern University, China Hua Lu Aalborg University, Denmark Chengkai Li The University of Texas at Arlington, USA Local Organization Co-chairs Xiaobing Mao Jiangxi University of Finance and Economics, China Guoqiong Liao Jiangxi University of Finance and Economics, China Registration Chair Yong Yang Jiangxi University of Finance and Economics, China Finance Chair Bo Shen Jiangxi University of Finance and Economics, China Web Chair Bo Yang Jiangxi University of Finance and Economics, China Steering Committee Liaison Weiyi Meng SUNY Binghamton, USA CCF DBS Liaison Xiaofeng Meng Renmin University of China, China Program Committee Alex Thomo University of Victoria, Canada Anirban Mondal Xerox Research Centre India, India Baihua Zheng Singapore Management University, Singapore Baoning Niu Taiyuan University of Technology, China Byron Choi Hong Kong Baptist University, SAR China Carson Leung University of Manitoba, Canada Ce Zhang Stanford University, USA Chengkai Li The University of Texas at Arlington, USA Chih-Hua Tai National Taipei University, China Cuiping Li Renmin University of China, China David Cheung The University of Hong Kong, SAR China Dejing Dou University of Oregon, USA De-Nian Yang Academia Sinica, Taiwan, China VIII Organization
Dongxiang Zhang National University of Singapore, Singapore Feida Zhu Singapore Management University, Singapore Feifei Li University of Utah, USA Fuzhen Zhuang ICT, Chinese Academy of Sciences, China Gang Chen Zhejiang University, China Gao Cong Nanyang Technological University, Singapore Giovanna Guerrini Università di Genova, Italy Guohui Li Huazhong University of Science and Technology, China Guoliang Li Tsinghua University, China Guoqiong Liao Jiangxi University of Finance and Economics, China Haibo Hu Hong Kong Polytechnic University, SAR China Hailong Sun Beihang University, China Hiroaki Ohshima Kyoto University, Japan Hongyan Liu Tsinghua University, China Hongzhi Wang Harbin Institue of Technology, China Hongzhi Yin The University of Queensland, Australia Hua Lu Aalborg University, Denmark Jae-Gil Lee Korea Advanced Institute of Science and Technology, Korea Jeffrey Xu Yu Chinese University of Hong Kong, SAR China Jiaheng Lu Renmin University of China Jianbin Qin The University of New South Wales, Australia Jianbin Huang Xidian University, China Jiannan Wang University of Berkerley, USA Jie Shao University of Electronic Science and Technology of China, China Jinchuan Chen Renmin University of China, China Jingfeng Guo Yanshan University, China Jiun-Long Huang National Chiao Tung University, Taiwan, China Jizhou Luo Harbin Institue of Technology, China Ju Fan National University of Singapore, Singapore Junfeng Zhou Yanshan University, China Junjie Yao East China Normal University, China Ke Yi Hong Kong University of Science and Technology, SAR China Kun Ren Yale University, USA Kyuseok Shim Seoul National University, South Korea Lei Zou Peking University, China Leong Hou U University of Macau, SAR China Lianghuai Yang Zhejiang University of Technology, China Lidan Shou Zhejiang University, China Lili Jiang Max Planck Institute for Informatics, Germany Ling Chen University of Technology, Sydney, Australia Luke Huan University of Kansas, USA Man Lung Yiu Hong Kong Polytechnic University, SAR China Muhammad Cheema Monash University, Australia Organization IX
Peiquan Jin University of Science and Technology of China, China Peng Wang Fudan University, China Qi Liu University of Science and Technology of China, China Qiang Wei Tsinghua University, China Qingzhong Li Shandong University, China Qinmin Hu East China Normal University, China Quan Zou Xiamen University, China Richong Zhang Beihang University, China Rui Zhang The University of Melbourne, Australia Rui Chen Samsung Research America, USA Saravanan Thirumuruganathan Qatar Computing Research Institute, Qatar Senjuti Basu Roy University of Washington, USA Shengli Wu Jiangsu University, China Shimin Chen Chinese Academy of Sciences, China Shinsuke Nakajima Kyoto Sangyo University, Japan Shuai Ma Beihang University, China Sourav Bhowmick National Taiwan University, China Takahiro Hara Osaka University, Japan Taketoshi Ushiama Kyushu University, Japan Tingjian Ge University of Massachusetts Lowell, USA Wang-Chien Lee Penn State University, USA Wei Wang University of New South Wales, Australia Weiwei Sun Fudan University, China Weiwei Ni Southeast University, China Wen-Chih Peng National Chiao Tung University, Taiwan, China Wenjie Zhang University of New South Wales, Australia Wolf-Tilo Balke TU-Braunschweig, Germany Wookey Lee Inha University, Korea Xiang Lian The University of Texas Rio Grande Valley, USA Xiangliang Zhang King Abdullah University of Science and Technology, Saudi Arabia Xiaochun Yang Northeast University, China Xiaofeng Meng Renmin University of China, China Xiaohui Yu Shandong University, China Xiaokui Xiao Nanyang Technological University, Singapore Xifeng Yan University of California at Santa Barbara, USA Xin Lin East China Normal University, China Xin Cao Queen’s University Belfast, UK Xin Wang Tianjin University, China Xingquan Zhu Florida Atlantic University, USA Xuanjing Huang Fudan University, China Yafei Li Henan University of Economics and Law, China Yang Liu Shandong University, China Yanghua Xiao Fudan University, China Yang-Sae Moon Kangwon National University, Korea X Organization
Yaokai Feng Kyushu University, Japan Yi Zhuang Zhejiang Gongshang University, China Yijie Wang National University of Defense Technology, China Yin Yang Hamad Bin Khalifa University, Qatar Ying Zhao Tsinghua University, China Yinghui Wu Washington State University, USA Yong Zhang Tsinghua University, China Yueguo Chen Renmin University of China, China Yunjun Gao Zhejiang University, China Zhaonian Zou Harbin Institue of Technology, China Zhenjie Zhang Advanced Digital Sciences Center, Singapore Zhifeng Bao RMIT University, Australia Zhiguo Gong University of Macau, SAR China Zhiyong Peng Wuhan University, China Zhoujun Li Beihang University, China Organization XI
Contents – Part I Data Mining More Efficient Algorithm for Mining Frequent Patterns with Multiple Minimum Supports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao Efficient Mining of Uncertain Data for High-Utility Itemsets . . . . . . . . . . . . 17 Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Vincent S. Tseng An Improved HMM Model for Sensing Data Predicting in WSN . . . . . . . . . 31 Zeyu Zhang, Bailong Deng, Siding Chen, and Li Li eXtreme Gradient Boosting for Identifying Individual Users Across Different Digital Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Rongwei Song, Siding Chen, Bailong Deng, and Li Li Two-Phase Mining for Frequent Closed Episodes . . . . . . . . . . . . . . . . . . . . 55 Guoqiong Liao, Xiaoting Yang, Sihong Xie, Philip S. Yu, and Changxuan Wan Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Xiaoxuan Wang, Lizhen Wang, Junli Lu, and Lihua Zhou Mining Top-k Distinguishing Sequential Patterns with Flexible Gap Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Chao Gao, Lei Duan, Guozhu Dong, Haiqing Zhang, Hao Yang, and Changjie Tang A Novel Chinese Text Mining Method for E-Commerce Review Spam Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Xiu Li and Xinwei Yan Spatial and Temporal Databases Retrieving Routes of Interest Over Road Networks . . . . . . . . . . . . . . . . . . . 109 Wengen Li, Jiannong Cao, Jihong Guan, Man Lung Yiu, and Shuigeng Zhou
Semantic-Aware Trajectory Compression with Urban Road Network . . . . . . . 124 Na Ta, Guoliang Li, Bole Chen, and Jianhua Feng Discovering Underground Roads from Trajectories Without Road Network . . . 137 Qiuge Song, Jiali Mao, and Cheqing Jin Ridesharing Recommendation: Whether and Where Should I Wait?. . . . . . . . 151 Chengcheng Dai Keyword-aware Optimal Location Query in Road Network . . . . . . . . . . . . . 164 Jinling Bao, Xingshan Liu, Rui Zhou, and Bin Wang Point-of-Interest Recommendations by Unifying Multiple Correlations. . . . . . 178 Ce Cheng, Jiajin Huang, and Ning Zhong Top-k Team Recommendation in Spatial Crowdsourcing . . . . . . . . . . . . . . . 191 Dawei Gao, Yongxin Tong, Jieying She, Tianshu Song, Lei Chen, and Ke Xu Explicable Location Prediction Based on Preference Tensor Model . . . . . . . . 205 Duoduo Zhang, Ning Yang, and Yuchi Ma Recommender Systems Random Partition Factorization Machines for Context-Aware Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Shaoqing Wang, Cuilan Du, Kankan Zhao, Cuiping Li, Yangxi Li, Yang Zheng, Zheng Wang, and Hong Chen A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Penghua Yu, Lanfen Lin, and Yuangang Yao RankMBPR: Rank-Aware Mutual Bayesian Personalized Ranking for Item Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 Lu Yu, Ge Zhou, Chuxu Zhang, Junming Huang, Chuang Liu, and Zi-Ke Zhang Unsupervised Expert Finding in Social Network for Personalized Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Junmei Ding, Yan Chen, Xin Li, Guiquan Liu, Aili Shen, and Xiangfu Meng An Approach for Clothing Recommendation Based on Multiple Image Attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Dandan Sha, Daling Wang, Xiangmin Zhou, Shi Feng, Yifei Zhang, and Ge Yu XIV Contents – Part I
SocialFM: A Social Recommender System with Factorization Machines . . . . 286 Juming Zhou, Dong Wang, Yue Ding, and Litian Yin Identifying Linked Data Datasets for sameAs Interlinking Using Recommendation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 Haichi Liu, Ting Wang, Jintao Tang, Hong Ning, Dengping Wei, Songxian Xie, and Peilei Liu Query-Biased Multi-document Abstractive Summarization via Submodular Maximization Using Event Guidance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 Rui Sun, Zhenchao Wang, Yafeng Ren, and Donghong Ji Graph Data Management Inferring Diffusion Network on Incomplete Cascade Data . . . . . . . . . . . . . . 325 Peng Dou, Sizhen Du, and Guojie Song Anchor Link Prediction Using Topological Information in Social Networks . . . 338 Shuo Feng, Derong Shen, Tiezheng Nie, Yue Kou, and Ge Yu Collaborative Partitioning for Multiple Social Networks with Anchor Nodes . . . 353 Fenglan Li, Anming Ji, Songchang Jin, Shuqiang Yang, and Qiang Liu A General Framework for Graph Matching and Its Application in Ontology Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Yuda Zang, Jianyong Wang, and Xuan Zhu Internet Traffic Analysis in a Large University Town: A Graphical and Clustering Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 Weitao Weng, Kai Lei, Kuai Xu, Xiaoyou Liu, and Tao Sun Conceptual Sentence Embeddings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 Yashen Wang, Heyan Huang, Chong Feng, Qiang Zhou, and Jiahui Gu Inferring Social Roles of Mobile Users Based on Communication Behaviors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 Yipeng Chen, Hongyan Li, Jinbo Zhang, and Gaoshan Miao Sparse Topical Coding with Sparse Groups . . . . . . . . . . . . . . . . . . . . . . . . 415 Min Peng, Qianqian Xie, Jiajia Huang, Jiahui Zhu, Shuang Ouyang, Jimin Huang, and Gang Tian Information Retrieval Differential Evolution-Based Fusion for Results Diversification of Web Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Chunlin Xu, Chunlan Huang, and Shengli Wu Contents – Part I XV
BMF: An Indexing Structure to Support Multi-element Check . . . . . . . . . . . 441 Chenyang Xu and Weixiong Rao Efficient Unique Column Combinations Discovery Based on Data Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454 Chao Wang, Shupeng Han, Xiangrui Cai, Haiwei Zhang, and Yanlong Wen Event Related Document Retrieval Based on Bipartite Graph . . . . . . . . . . . . 467 Wenjing Yang, Rui Li, Peng Li, Meilin Zhou, and Bin Wang SPedia: A Semantics Based Repository of Scientific Publications Data . . . . . 479 Muhammad Ahtisham Aslam and Naif Radi Aljohani A Set-Based Training Query Classification Approach for Twitter Search . . . . 491 Qingli Ma, Ben He, Jungang Xu, and Bin Wang NBLucene: Flexible and Efficient Open Source Search Engine . . . . . . . . . . . 504 Zhaohua Zhang, Benjun Ye, Jiayi Huang, Rebecca Stones, Gang Wang, and Xiaoguang Liu Context-Aware Entity Summarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Jihong Yan, Yanhua Wang, Ming Gao, and Aoying Zhou Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 XVI Contents – Part I
Contents – Part II Privacy and Trust Detecting Anomalous Ratings Using Matrix Factorization for Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Zhihai Yang, Zhongmin Cai, and Xinyuan Chen A Novel Spatial Cloaking Scheme Using Hierarchical Hilbert Curve for Location-Based Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Ningning Cui, Xiaochun Yang, and Bin Wang Efficient Privacy-Preserving Content-Based Image Retrieval in the Cloud . . . 28 Kai Huang, Ming Xu, Shaojing Fu, and Dongsheng Wang Preserving the d-Reachability When Anonymizing Social Networks . . . . . . . 40 Xiangyu Liu, Jiajia Li, Dahai Zhou, Yunzhe An, and Xiufeng Xia Personalized Location Anonymity - A Kernel Density Estimation Approach . . . 52 Dapeng Zhao, Jiansong Ma, Xiaoling Wang, and Xiuxia Tian Detecting Data-model-oriented Anomalies in Parallel Business Process . . . . . 65 Ning Yin, Shanshan Wang, Hongyan Li, and Lilue Fan Learning User Credibility on Aspects from Review Texts . . . . . . . . . . . . . . 78 Yifan Gao, Yuming Li, Yanhong Pan, Jiali Mao, and Rong Zhang Detecting Anomaly in Traffic Flow from Road Similarity Analysis . . . . . . . . 92 Xinran Liu, Xingwu Liu, Yuanhong Wang, Juhua Pu, and Xiangliang Zhang Query Processing and Optimization PACOKS: Progressive Ant-Colony-Optimization-Based Keyword Search over Relational Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Ziyu Lin, Qian Xue, and Yongxuan Lai Enhanced Query Classification with Millions of Fine-Grained Topics . . . . . . 120 Qi Ye, Feng Wang, Bo Li, and Zhimin Liu A Hybrid Machine-Crowdsourcing Approach for Web Table Matching and Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Chunhua Li, Pengpeng Zhao, Victor S. Sheng, Zhixu Li, Guanfeng Liu, Jian Wu, and Zhiming Cui
An Update Method for Shortest Path Caching with Burst Paths Based on Sliding Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Xiaohua Li, Ning Wang, Kanggui Peng, Xiaochun Yang, and Ge Yu Low Overhead Log Replication for Main Memory Database System . . . . . . . 159 Jinwei Guo, Chendong Zhang, Peng Cai, Minqi Zhou, and Aoying Zhou Diversification of Keyword Query Result Patterns. . . . . . . . . . . . . . . . . . . . 171 Cem Aksoy, Ananya Dass, Dimitri Theodoratos, and Xiaoying Wu Efficient Approximate Substring Matching in Compressed String . . . . . . . . . 184 Yutong Han, Bin Wang, and Xiaochun Yang Top-K Similarity Search for Query-By-Humming . . . . . . . . . . . . . . . . . . . . 198 Peipei Wang, Bin Wang, and Shiying Luo Social Media Restricted Boltzmann Machines for Retweeting Behaviours Prediction. . . . . . 213 Xiang Li, Lijuan Xie, Yong Tan, and Qiuli Tong Cross-Collection Emotion Tagging for Online News . . . . . . . . . . . . . . . . . . 225 Li Yu, Xue Zhao, Chao Wang, Haiwei Zhang, and Ying Zhang Online News Emotion Prediction with Bidirectional LSTM . . . . . . . . . . . . . 238 Xue Zhao, Chao Wang, Zhifan Yang, Ying Zhang, and Xiaojie Yuan Learning for Search Results Diversification in Twitter . . . . . . . . . . . . . . . . . 251 Ying Wang, Zhunchen Luo, and Yang Yu Adjustable Time-Window-Based Event Detection on Twitter . . . . . . . . . . . . 265 Qinyi Wang, Jieying She, Tianshu Song, Yongxin Tong, Lei Chen, and Ke Xu User-IBTM: An Online Framework for Hashtag Suggestion in Twitter . . . . . 279 Jia Li and Hua Xu Unifying User and Message Clustering Information for Retweeting Behavior Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Bo Jiang, Jiguang Liang, Ying Sha, Lihong Wang, Zhixin Kuang, Rui Li, and Peng Li KPCA-WT: An Efficient Framework for High Quality Microblog Extraction in Time-Frequency Domain. . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 Min Peng, Xinyuan Dai, Kai Zhang, Guanyin Zeng, Jiahui Zhu, Shuang Ouyang, Qianqian Xie, and Gang Tian XVIII Contents – Part II
Big Data Analytics Active Learning Method for Constraint-Based Clustering Algorithms. . . . . . . 319 Lijun Cai, Tinghao Yu, Tingqin He, Lei Chen, and Meiqi Lin An Effective Cluster Assignment Strategy for Large Time Series Data. . . . . . 330 Damir Mirzanurov, Waqas Nawaz, JooYoung Lee, and Qiang Qu AdaWIRL: A Novel Bayesian Ranking Approach for Personal Big-Hit Paper Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Chuxu Zhang, Lu Yu, Jie Lu, Tao Zhou, and Zi-Ke Zhang Detecting Live Events by Mining Textual and Spatial-Temporal Features from Microblogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Zhejun Zheng, Beihong Jin, Yanling Cui, and Qiang Ji A Label Correlation Based Weighting Feature Selection Approach for Multi-label Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Lu Liu, Jing Zhang, Peipei Li, Yuhong Zhang, and Xuegang Hu Valuable Group Trajectory Pattern Mining Directed by Adaptable Value Measuring Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 Xinyu Huang, Tengjiao Wang, Shun Li, and Wei Chen DualPOS: A Semi-supervised Attribute Selection Approach for Symbolic Data Based on Rough Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 Jianhua Dai, Huifeng Han, Hu Hu, Qinghua Hu, Jinghong Zhang, and Wentao Wang Semi-supervised Clustering Based on Artificial Bee Colony Algorithm with Kernel Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Jianhua Dai, Huifeng Han, Hu Hu, Qinghua Hu, Bingjie Wei, and Yuejun Yan Distributed and Cloud Computing HMNRS: A Hierarchical Multi-source Name Resolution Service for the Industrial Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Yang Liu, Guoqiang Fu, and Xinchi Li Optimizing Replica Exchange Strategy for Load Balancing in Multienant Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Teng Liu, Qingzhong Li, Lanju Kong, Lei Liu, and Lizhen Cui ERPC: An Edge-Resources Based Framework to Reduce Bandwidth Cost in the Personal Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 Shaoduo Gan, Jie Yu, Xiaoling Li, Jun Ma, Lei Luo, Qingbo Wu, and Shasha Li Contents – Part II XIX
Multidimensional Similarity Join Using MapReduce . . . . . . . . . . . . . . . . . . 457 Ye Li, Jian Wang, and Leong Hou U Real-Time Logo Recognition from Live Video Streams Using an Elastic Cloud Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Jianbing Ding, Hongyang Chao, and Mansheng Yang Profit Based Two-Step Job Scheduling in Clouds . . . . . . . . . . . . . . . . . . . . 481 Shuo Zhang, Li Pan, Shijun Liu, Lei Wu, and Xiangxu Meng A Join Optimization Method for CPU/MIC Heterogeneous Systems . . . . . . . 493 Kailai Zhou, Hong Chen, Hui Sun, Cuiping Li, and Tianzhen Wu GFSF: A Novel Similarity Join Method Based on Frequency Vector . . . . . . . 506 Ziyu Lin, Daowen Luo, and Yongxuan Lai Demo Papers SHMS: A Smart Phone Self-health Management System Using Data Mining. . . 521 Chuanhua Xu, Jia Zhu, Zhixu Li, Jing Xiao, Changqin Huang, and Yong Tang MVUC: An Interactive System for Mining and Visualizing Urban Co-locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 Xiao Wang, Hongmei Chen, and Qing Xiao SNExtractor: A Prototype for Extracting Semantic Networks from Web Documents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Chi Zhang, Yanhua Wang, Chengyu Wang, Wenliang Cheng, and Xiaofeng He Crowd-PANDA: Using Crowdsourcing Method for Academic Knowledge Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Zhaoan Dong, Jiaheng Lu, and Tok Wang Ling LPSMon: A Stream-Based Live Public Sentiment Monitoring System . . . . . . 534 Kun Ma, Zijie Tang, Jialin Zhong, and Bo Yang DPBT: A System for Detecting Pacemakers in Burst Topics. . . . . . . . . . . . . 537 Guozhong Dong, Wu Yang, Feida Zhu, and Wei Wang CEQA - An Open Source Chinese Question Answer System Based on Linked Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Zeyu Du, Yan Yang, Qinming Hu, and Liang He XX Contents – Part II
OSSRec:An Open Source Software Recommendation System Based on Wisdom of Crowds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 Mengwen Chen, Gang Yin, Chenxi Song, Tao Wang, Cheng Yang, and Huaimin Wang Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Contents – Part II XXI
Data Mining
More Efficient Algorithm for Mining Frequent Patterns with Multiple Minimum Supports Wensheng Gan1 , Jerry Chun-Wei Lin1(B) , Philippe Fournier-Viger2 , and Han-Chieh Chao1,3 1 School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China wsgan001@gmail.com, jerrylin@ieee.org 2 School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China philfv@hitsz.edu.cn 3 Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien County, Taiwan hcc@ndhu.edu.tw Abstract. Frequent pattern mining (FPM) is an important data mining task, having numerous applications. However, an important limitation of traditional FPM algorithms, is that they rely on a single minimum sup- port threshold to identify frequent patterns (FPs). As a solution, several algorithms have been proposed to mine FPs using multiple minimum sup- ports. Nevertheless, a crucial problem is that these algorithms generally consume a large amount of memory and have long execution times. In this paper, we address this issue by introducing a novel algorithm named efficient discovery of Frequent Patterns with Multiple minimum supports from the Enumeration-tree (FP-ME). The proposed algorithm discovers FPs using a novel Set-Enumeration-tree structure with Multiple mini- mum supports (ME-tree), and employs a novel sorted downward closure (SDC) property of FPs with multiple minimum supports. The proposed algorithm directly discovers FPs from the ME-tree without generating candidates. Furthermore, an improved algorithms, named FP-MEDiffSet, is also proposed based on the DiffSet concept, to further increase mining performance. Substantial experiments on real-life datasets show that the proposed approaches not only avoid the “rare item problem”, but also efficiently and effectively discover the complete set of FPs in transac- tional databases. Keywords: Frequent patterns · Multiple minimum supports · Sorted downward closure property · Set-enumeration-tree · DiffSet 1 Introduction In the process of knowledge discovery in database (KDD) [2,3], many approaches have been proposed to discover more useful and invaluable information from c Springer International Publishing Switzerland 2016 B. Cui et al. (Eds.): WAIM 2016, Part I, LNCS 9658, pp. 3–16, 2016. DOI: 10.1007/978-3-319-39937-9 1
4 W. Gan et al. huge databases. Among them, frequent pattern mining (FPM) and association rule mining (ARM) [2–4] have been extensively studied. Most studies in FPM or ARM focus on developing efficient algorithms to mine frequent patterns (FPs) in a transactional database, in which the occurrence frequency of each item- set is no less than a specified number of customer transactions w.r.t. the user- specified minimum support threshold (called minsup). However, they suffer from an important limitation, which is to utilize a single minimum support threshold as the measure to discover the complete set of FPs. Using a single threshold to assess the occurrence frequencies of all items in a database is inadequate since each item is different and should not all be treated the same. Hence, it is hard to carry out a fair measurement of the frequencies of itemsets using a single minimum support when mining FPs. For market basket analysis, a traditional FPM algorithm may discover many itemsets that are frequent but generate a low profit and fail to dis- cover itemsets that are rare but generate a high profit. For example, clothes i.e., {shirt, tie, trousers, suits} occur much more frequent than {diamond} in a supermarket, and both have positive contribution to increase the profit amount. If the value of minsup is set too high, though the rule {shirt, tie ⇒ trousers} can be found, we would never find the rule {shirt, tie ⇒ diamond}. To find the second rule, we need to set the minsup very low. However, this will cause lots of meaningless rules to be found at the same time. This is the co-called “rare item problem” [7]. To address this issue, the problem of frequent pattern min- ing with multiple minimum supports (FP-MMS) has been studied. Liu et al. [7] introduced the problem of FP-MMS and proposed the MSApriori algorithm by extending the level-wise Apriori algorithm. The goal of FP-MMS is to discover the useful set of itemsets that are “frequent” for the users. It allows the users to free set multiple minimum support thresholds instead of an uniform mini- mum support threshold to reflect different natures and frequencies of all items. Some approaches have been designed for the mining task of FP-MMS, such as MSApriori [7], CFP-growth [11], CFP-growth++ [10], etc. The state-of-the-art CFP-growth++ was proposed by extending the FP-growth [4] approach to mine FPs from a condensed CFP-tree structure. However, the mining efficiency of them is still a major problem. Since the previous studies of FP-MMS still suffer the time-consuming and memory usage problems, it is thus quite challenging and critically important to design an efficient algorithm to solve this problem. In this paper, we propose a novel mining framework named mining frequent patterns from the Set-enumeration-tree with multiple minimum supports to address this important research gap. Major contributions are summarized as follows: – Being different from the Apriori-like and FP-growth-based approaches, we propose a novel algorithm for directly extracting FPs with multiple minimum supports from the Set-enumeration-tree (abbreviated as FP-ME). It allows the user to specify multiple minimum support thresholds to reflect different natures and frequencies of items. This increases the applicability of FPM to real-life situations.
More Efficient Algorithm for Mining Frequent Patterns 5 – Based on the proposed Set-Enumeration-tree with Multiple minimum supports (ME-tree), a new sorted downward closure (SDC) property of FPs in ME-tree holds. Therefore, the baseline algorithm FP-ME can directly discover FPs by spanning the ME-tree with the SDC property, without the candidate generate- and-test, thus greatly reduce the running time and memory consumption. – The DiffSet concept is further extend in the improved FP-MEDiffSet algorithm to greatly speed up the process of mining FPs. – Extensive experiments show that the proposed FP-ME algorithm is more effi- cient than the state-of-the-art CFP-growth++ algorithm for mining FPs in terms of runtime, effectiveness of prune strategies and scalability, especially the improved algorithm considerably outperforms the baseline algorithm. 2 Related Work Up to now, many approaches have been extensively developed to mine FPs. Since only a single minsup value is used for the whole database, the model of ARM implicitly assumes that all the items in the database have similar occurrence frequencies. However, as most of the real-world databases are non-uniform in nature, mining FPs with a single minsup (or mincof ) constraint leads to the following problems: (i) If minsup is set too high, we will not find the patterns involving rare items. (ii) In order to find the patterns that involve both fre- quent and rare items, we have to set minsup very low. However, this may cause combinatorial explosion, producing many meaningless patterns. The problem of frequent pattern mining with multiple minimum support thresholds has been extensively studied, and algorithms such as MSApriori [7], CFP-growth [11], CFP-growth++ [10], REMMAR [8] and FQSP-MMS [6] have been proposed, among others [9]. MSApriori extends the well-known Apriori algorithm to mine FPs or ARs by considering multiple minimum support thresh- olds [7]. The major idea of MSApriori is that by assigning specific minimum item support (MIS) values to each item, rare ARs can be discovered without gener- ating a large number of meaningless rules. MSApriori mines ARs in a level-wise manner but suffers from the problem of “pattern explosion” since it relies on a generate-and-test approach. Lee et al. [9] then proposed a fuzzy mining algorithm for discovering useful fuzzy association rules with multiple minimum supports by using maximum constraints. An improved tree-based algorithm named CFP- growth [11] was then proposed to directly mine frequent itemsets with multiple thresholds using the pattern growth method based on a new MIS-Tree struc- ture. An enhanced version of CFP-growth named CFP-growth++ [10] was also proposed, it employs LMS (least minimum support) instead of MIN to reduce the search space and improve performance. LMS is the least MIS value amongst all MIS values of frequent items. Moreover, three improved strategies were also presented to reduce the search space and runtime. However, it is still too time- consuming and memory cost.
6 W. Gan et al. Table 1. An example database TID Transaction T1 a, c, d T2 a, d, e T3 b, c T4 a, c, e T5 a, b, c, d, e T6 b, d T7 a, b, c, e T8 b, c, d T9 c, d, e T10 a, c, d Table 2. Derived FPs Itemset MIS × |D| sup Itemset MIS × |D| sup (a) 4 6 (ae) 4 4 (b) 5 5 (bc) 3 4 (c) 3 8 (cd) 3 5 (d) 6 7 (ce) 3 4 (ac) 3 5 (acd) 3 3 (ad) 4 4 (ace) 3 3 3 Preliminaries and Problem Statement Let I = {i1, i2, . . . , im} be a finite set of m distinct items. A transactional database D = {T1, T2, . . . , Tn}, where each transaction Tq ∈ D is a subset of I, contains several items with their purchase quantities q(ij, Tq), and has an unique identifier, TID. A corresponding multiple minimum supports table, MMS-table = {ms1, ms2, . . . , msm}, indicates the user-specified minimum sup- port value msj of each item ij. A set of k distinct items X = {i1, i2, . . . , ik} such that X ⊆ I is said to be a k-itemset. An itemset X is said to be contained in a transaction Tq if X ⊆ Tq. An example database is shown in Table 1. It consists of 10 transactions and 5 items, denoted from (a) to (e), respectively. For example, transaction T1 contains items a, c and d. The minimum support value of each item, denoted as ms, is defined and as shows the MMS-table = {ms(a): 40 %; ms(b): 50 %; ms(c): 30 %; ms(d): 60 %; ms(e): 100 %}. Definition 1. The number of transactions that contains an itemset is known as the occurrence frequency of that itemset. This is also called the support count of the itemset. The support of an itemset X, denoted by sup(X), is the number of transactions containing X w.r.t. X ⊆ Tq. Definition 2. The minimum support threshold of an item ij in a database D, which is related to minsup, is redenoted as ms(ij) in this paper. A structure called MMS-table indicates the minimum support threshold of each item in D and is defined as: MMS-table = {ms1, ms2, . . ., msm}. Definition 3. The minimum item support value of a k-itemset X = {i1, i2, . . . , ik} in D is denoted as MIS(X), and defined as the smallest ms value for items in X, that is: MIS(X) = min{ms(ij)|ij ∈ X}. For example, MIS(a) = min{ms(a)} = 40 %, MIS(ae) = min{ms(a), ms(e)} = min{40 %, 100 %} = 40 %, and MIS(ace) = min{ms(a), ms(c), ms(e)} = min{40 %, 30 %, 100 %} = 30 %. The extended model enables the
More Efficient Algorithm for Mining Frequent Patterns 7 user to simultaneously specify high minsup for a pattern containing only fre- quent items and low minsup for a pattern containing rare items. Thus, efficiently addressing the “rare item problem”. Definition 4. An itemset X in a database D is called a frequent pattern (FP) iff its support count is no less than the minimum itemset support value of X, such that sup(X) ≥ MIS(X) × |D|. Definition 5. Let be a transactional database D (|D| = n) and a MMS-table, which defines the minimum support thresholds {ms1, ms2, . . . , msm} of each item in D. The problem of mining FPs from D with multiple minimum supports (FP-MMS) is to find all itemsets X having a support no less than MIS(X)×|D|. For the running example, the derived complete set of FPs is shown in Table 2. 4 Proposed FP-ME Algorithm for FP-MMS 4.1 Proposed ME-Tree Based on the previous studies, the search space of mining frequent patterns with multiple minimum supports can be represented as a lattice structure [12] or a Set-enumeration tree [12], both of them for the running example are respectively shown in Fig. 1. Note that the well-known downward closure property in associ- ation rule mining does not hold for FP-MMS. For example, the item (e) is not a FP but its supersets (ae) and (ace) are FPs in the running example. To solve this problem, Liu et al. [7] proposed a concept called sorted closure property, which assumes that all items within an itemset are sorted in increasing order of their minimum supports. Property 1. If a sorted k-itemset {i1, i2,. . . , ik}, for k ≥ 2 and MIS(i1) ≤ MIS(i2) ≤ . . . ≤ MIS(ik), is frequent, then all of its sorted subsets with k − 1 items are frequent, except for the subset {i2, i3, . . . , ik} [6]. Definition 6 (Total order ≺ on items). Without loss of generality, assume that items in each transaction of a database are sorted according to the lexi- cographic order. Furthermore, assume that the total order ≺ on items in the designed ME-tree is the ascending order of items MIS values. Definition 7 (Set-enumeration-tree with multiple minimum sup- ports). A ME-tree is a sorted Set-enumeration tree using the defined total order ≺ on items. Definition 8 (Extension nodes in the ME-tree). In the designed ME-tree with the total order ≺, all child nodes of any tree node are called its extension nodes. Since MIS(c) MIS(a) MIS(b) MIS(d) MIS(e), the total order ≺ on items in the ME-tree is c ≺ a ≺ b ≺ d ≺ e. The ME-tree for the running example is illustrated in Fig. 2, and the following lemmas are obtained.
8 W. Gan et al. e c d { } b a abc abd abce abce abde abe 3 level 5 level ab ac ad ae ade bcd bce acde bcde abcde bc bd be cd ce de cde bde acd ace 1 level 2 level 4 level e c d { } b a abc abd abcd abce abde abe 3 level 4 level ab ac ad ae ade bcd bce acde bcde abcde bc bd be cd ce de cda bde acd ace 1 level 2 level 5 level (a) A subset of lattice structure (b) A set enumeration tree Fig. 1. The search space presentation of FP-MMS. e b d { } a c cab cad cabd cabe cade cae ca cb cd ce cde abd abe cbde abde cabde ab ad ae bd be de bde ade cbe cde skipped nodes visited and pruned nodes visited nodes MIS(c) MIS(a) MIS(b) MIS(d) MIS(e) e b d { } a c cad cabd cabe cade cae ca cb cd ce cde abd abe cbde abde cabde ab ad ae bd be de bde ade cbd cbe cab (a) (b) Fig. 2. Applied pruning strategies in the ME-tree. Lemma 1. The complete search space of the proposed FP-MMS algorithm can be represented by a ME-tree where items are sorted according to the ascending order of the MIS values on items. Lemma 2. The support of a node in the ME-tree is no less than the support of any of its child nodes (extension nodes). Proof. Let Xk be a node in the ME-tree containing X items, and let Xk−1 be any parent node of Xk containing (k-1) items. It is straightforward from the well-known Apriori property that sup(Xk ) ≤ sup(Xk−1 ), this lemma can be proven. Lemma 3. The MIS of a node in the ME-tree equals to the MIS of any of its child nodes (extension nodes).
More Efficient Algorithm for Mining Frequent Patterns 9 4.2 Proposed Pruning Strategies It is important to note that the sorted downward closure (SDC) property of FPs in the ME-tree can only guarantee partial anti-monotonicity for FPs, but not general anti-monotonicity. In other words, the SDC property holds for any extensions (child nodes) of a given node, but it may not hold for any supersets of that node. Thus, if the SDC property of FPs is used to determine if all supersets of an itemset should be explored, some FPs may not be found. For instance, in the running example database, the item (e) is not a FP since sup(e) = 5 (10), while its supersets (ae), (ce) and (ace) are FPs, as shown in Table 2. It is thus incorrect to directly determine the FPs based only on the proposed SDC property. In MSApriori and CFP-growth++, it was shown that the MIN/LMS concept can guarantee the global anti-monotonicity of frequent patterns with multiple minimum supports and ensure the completeness of the set of derived FPs. To address this problem, we further adopt the MIN/LMS concept in the proposed FP-ME algorithm. Definition 9 (Least minimum support, LMS). The least minimum support (LMS) refers to the lowest minsup of all frequent patterns. Therefore, the LMS in a database is always equal to the lowest MIS value among all frequent items. Thus, the LMS is equal to the lowest value in the MMS-table and is defined as min{ms(i1), ms(i2), . . . , ms(im)}, where m is the total number of items in a database. For example, the LMS of Table 2 is calculated as LMS = min{ms(a), ms(b), ms(c), ms(d), ms(e)} = min{40 %, 50 %, 30 %, 60 %, 100 %} = 30 %. Property 2. If X = {i1, i2,. . . , ik} ⊆ I, where 1 ≤ k ≤ n, is a pattern such that sup(X) LMS, then sup(X) min{MIS(i1), MIS(i2), . . . , MIS(ik)}, it never could be a frequent pattern. Property 3. If X and Y are two patterns such that X ⊂ Y and sup(X) LMS, then sup(Y ) LMS. It indicates that LMS guarantees the global anti- monotonicity of frequent patterns with multiple minimum supports. Proof. Let be an itemset X such that X is a subset of Y . Thus, sup(Y ) ≤ sup(X). The relationship sup(Y ) ≤ sup(X) LMS holds. Note that the set of 1-items which having sup(X) ≥ LMS is denoted as LMS-FP1 , the following theorem can be obtained. Theorem 1. Assume that 1-itemsets which having a MIS lower than LMS are discarded and that the sorted downward closure (SDC) property is applied. We have that if an itemset is not a LMS-FP1 , then it is not a FP as well as all its supersets. Proof. Let Xk−1 be a (k-1)-itemset and its superset k-itemset is denoted as Xk . Since Xk−1 ⊆ Xk , (1) For a LMS-FP1 , sup(X) ≥ LMS; (2) Since items are sorted by ascending order of MIS values, sup(Xk−1 ) ≥ sup(Xk )
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CHAPTER V THE COMING OF CHRISTIANITY Some there were who had heard of Christ in the old days, but a band of monks landing on the coast of Kent brought the news again to this country. Pope Gregory had sent Augustine from Rome to tell the Saxons about Christ, for he was sorry that they loved Odin and Thor, and did not know any other god. Ethelbert, the King of Kent, had a Christian wife, and he was very anxious to know what these strangers had to say about the new God. But he was afraid that they might know how to work charms and to call out wicked spirits, so he let Augustine and his monks preach to the people out of doors, for he thought that they could not harm any one in the open air. When the Roman monks preached, many people became Christians, but the old Saxon poets sang sorrowful stories of Odin's anger, and how the gods had left the world for ever because the people were not faithful. Bede tells a story of how the old wise men of Northumbria met together to decide whether they would give up the old gods for Christ or not, and as they sat in solemn silence, thinking of this great thing, an old man rose and said, The present life of man, O King, seems to me like the swift flight of a sparrow, who on a wintry night darts into the hall, as we sit at supper. He flies from the storms of wind and rain outside, and for a brief space abides in the warmth and light, and then vanishes again into the darkness whence he came. So is the life of man, for we know not whence we came nor whither we go. Therefore if this stranger can tell us anything more certain, we should hearken gladly to him. Thus, they became Christians. They built churches in their villages; first of wood, then of stone.
Many Christian teachers then came to England and built homes or monasteries, wherever they went, first of rough timber, then of stone. They made clearings in the forests and drained the fenlands, and the people followed and built houses for themselves near the monasteries, for they found that they could learn many things from the monks. The sick, the poor, the tired and the old were always welcome, and travellers too were glad to rest there, for there were no inns in those days. The monks were ruled by an abbot, and the nuns, who lived in other houses, by an abbess. They took a vow of poverty and thought that they served God best by giving their time to prayer and praise. They loved their monastery, and, as the centuries went by, they made it more and more beautiful. The people gave rich offerings and builders came from foreign lands, skilled in stonework and other arts. Carvings were made for the church, pictures were painted on the walls, and flowers and trees were brought from the Holy Land to plant in the gardens. In this way came the cedar trees and the juniper, and certain plants that now grow wild in parts of the country like the poisonous hellebore, the grape hyacinth and the little fritillary or snake's head. Great men brought gifts of frankincense and myrrh, to be burned in the church on holy days, or jewels for the altar, and silk from the east for hangings, but the greatest treasure of all was the relic. People would travel many miles to see this, for those who saw it could be healed of their sickness or forgiven for their sins. There were many curious relics. There were little bits of wood, that men believed belonged to the real cross, on which Christ was crucified, and thorns, which were said to have come from His crown. S. Louis, King of France, built the beautiful Sainte Chapelle in Paris, where he might keep the crown of thorns, which the Crusaders brought from Palestine. The monastery was usually built round a square garden or lawn. On one side was the church, on another the hall and large kitchens and pantries, for there were often visitors, some of high estate, and they must be royally feasted. In the Rule of S. Benedict it was written, Let
all guests who come to the monastery be entertained like Christ Himself; because He will say, 'I was a stranger and ye took me in'. The guest-house must stand apart so that the guests, who are never wanting in a monastery, may not disquiet the brethren by their untimely arrivals. Anyone could claim a lodging for two nights, and in a few monasteries there was stabling provided for as many as three hundred horses. There was a long dormitory where the monks slept. It was the custom for them to get up at midnight to make a procession into the church by the night stairs. There they said matins and lauds (the last three psalms), and then returned to the dormitory to sleep if it were winter until daybreak, if summer till sunrise. Only those who had worked hard in the fields all day were excused. They dressed by the light of the wicks set in oil in little bowls at either end of the dormitory. In the cloisters were troughs for washing before meals, filled with water by taps; and above were little cupboards for towels. Some monasteries had a library, for they were quite rich in books. Then there was a writing room, where the scribes were busy making beautiful copies of the precious books, some skilled in writing, others in painting and illuminating. When the writing was done, the artist brought his colours to make the capital letters and the little pictures in the text. There was music to be copied too, and the accounts of the Abbey must be kept neatly. Sometimes a chronicle was made of great events that happened. It is from such books as these that we have learned much about the story of the country.
THE ABBEY OF CITEAUX A. Round this court, stables and barns. H. Guest houses and abbot's quarters. N. The Church. I. The kitchen. K. The dining hall. M. The dormitories. P. Cells of the scribes. R. The hospital.
A SERVICE IN THE CHAPEL The monks led peaceful lives in days when most men were busy about war. The monks divided the hours between sunrise and sunset into twelve equal parts, so it happened that the hour in winter was twenty minutes shorter than in summer. Every three hours, there was a service in church, prime at the first, terce at the third, sext at the sixth and none at the ninth. After prime, on summer mornings, the monks were summoned by the Abbot to the chapter house and there each man received his task. The latest business was talked about and plans were made for the coming guests. Then each monk went to his business, some to the gate to give food to the poor and help to the sick, some to work in the orchard and garden, to spin or to weave, though in some monasteries this kind of work was done for the brethren. They had their first meal at midday in the hall in silence. While they ate, one of their number, who had already had his meal, would read to them from a book of sermons or the Lives of the Saints. After grace, the Miserere (Psalm 51) was sung through the cloister. In summer, they would rest in the afternoon, in the dormitory or perhaps in the cloister, on the sunny south side, where they could read or think or pray. In winter, they worked at this time, because their nights were
long. Vespers was read at sunset, then came supper. Compline ended the day, but it sometimes happened that they lingered in the warming- house to chat with one another, but this was against rules. Kings and princes found out what wise counsellors these men were and brought them to the courts to help them govern, though this was against the rules of the monastic orders. Then, in those days, Abbots began to ride forth like princes, monasteries were full of treasure and monks forsook the humbler ways of life they had once followed.
CHAPTER VI ALFRED AND THE DANES After the Saxons had been in England many years, when their weapons had grown rusty and they had almost forgotten how to fight, bands of Danes came sailing over the North Sea to plunder the land. God Almighty sent forth these fierce and cruel people like swarms of bees, says the chronicler. First, they carried away the beautiful things from the monasteries and churches, and then they came to live here. They drove the Saxons from their houses or built new villages by the side of the old ones. We know that they must have settled in Yorkshire and Lincolnshire, in Westmorland and Cumberland, because they gave Danish names to many places, such as Grimsby (Grim's town), Whitby, Appleby. In those days, the Danes grew very bold. Ships came from the west ready for war with grinning heads and carven beaks, runs the legend, the golden war banner shining in the bows. They tried to conquer the west and south, as well as the north and east. In the land of the West Saxons, many battles were fought, and still the little band of hungry, worn-out soldiers stood at bay. It was at this time that Alfred was made King and, like his father and brothers, was soon defeated and driven into Athelney, a little island in the west in the midst of a great swamp. There, he spent the winter drilling his soldiers and making plans to drive away the Danes in the spring time. A story is told of how he went into the Danish camp as a bard. He carried a harp, and while the mead cup was handed round, he sang the old sagas. When the feast was done and the chess board was brought out, the captains talked about the war, as they played their favourite game. So Alfred heard their plans.
The Danes were surprised when the spring came, for Alfred drove them out of his kingdom and made them promise never to come into the land of the West Saxons again. But he did not try to drive them out of England, for he knew that it would be many years before his people would be strong enough, perhaps not until his own children were grown up. So he worked hard all his life to make his people good soldiers and thoughtful men, in order that, when the time came, they could drive the enemy across the seas and rule over the whole land in their stead. Formerly, said the King, foreigners sought wisdom and learning in this land, now we should have to get them from abroad if we would have them. Alfred found his nobles careless and idle, they loved hunting and feasting and thought very little about ruling a kingdom or leading an army. They were too old to learn, but the king made up his mind that their children should grow up good soldiers and wise rulers. So he made a school at his court for these boys. There they learned the art of war and many other things too. They read the history of their own country from Bede's Book, that had been kept at York. This book was written in Latin, so the King had to have it translated for them. He had heard of the fame of a great writer, Asser, who lived in South Wales. Messages were sent to him to ask him to come to Alfred's court to write the history of the reign. Asser did not wish to leave his beautiful home, but in the end, he promised to stay for six months every year; that is why we know so much about this great King. Alfred turned into English some beautiful old Latin books that taught men how to rule well, and in the margins he himself wrote what he thought wise counsel. Two of these books had been written by Pope Gregory who sent Augustine to England, and at the beginning of one of them there are these words, Alfred, King, turned each word of me into English and sent me to his writers, north and south, and bade
them make more such copies that he might send them to the bishops. Alfred loved reading and he wrote down all the wise sayings that he found. Asser tells the story of how the King came to do this. When we were one day sitting together in the royal chamber and were holding converse upon divers topics, as our wont was, it chanced that I repeated to him a quotation from a certain book. And when he had listened attentively to this with all his ears, and had carefully pondered it in the deep of his mind, suddenly he showed me a little book which he carried constantly in the fold of his cloak. In it were written the Daily Course and certain psalms and some prayers, which he had read in his youth, and he commanded that I should write that quotation in the same little book. And when he urged me to write that as quickly as possible, I said to him, 'Are you willing that I should write the quotation apart by itself on a small leaf? For we know not that at some time we shall not find some other such quotation or more than one, which will please you: and if it should so turn out unexpectedly we shall rejoice that we have kept this apart from the rest.' And when he heard this, he said 'Your counsel is good.' And I, hearing this and being glad, made ready a book of several leaves, in haste, and at the beginning of it I wrote that quotation according to his command. And on the same day, by his order, I wrote in the same book no less than three other quotations pleasing to him, as I had foretold. This book he used to call his handbook, because with the utmost care he kept it at his hand day and night and in it he found, as he said, no small comfort. Alfred desired to hear of other lands, but there were hardly any maps in those days and no books of geography. Great travellers were welcomed at his court, for, when he was very young, he had paid a visit to Rome and had seen a little of foreign lands. Othere, the famous seaman, who had sailed in the Arctic regions, came to tell his
stories of the frozen seas that men could walk upon and of the strange midnights when the sun shone as bright as by day. Othere spoke of whales and walruses and he brought their tusks of fine ivory to show the King. Wulfstan came, too, and he had travelled in Prussia and brought stories of a land rich in honey and fish. Travellers came from the hot lands, from India and the far east. They brought presents of tiger skins and spices, of rich silks and jewels. They told stories of wonderful deserts, of the high snowy mountains and thick jungles, that they had passed on their long journey. The King delighted to read of elephants and lions and of the beast we call lynx that men said could see through trees and even stones. Or what shall I say, says the chronicler, concerning the daily intercourse with the nations which dwell from the shores of Italy unto the uttermost bounds of Ireland? for I have seen and read letters and gifts sent to Alfred by Elias, patriarch of Jerusalem. In this way the West Saxon folk heard of great, unknown countries and peoples, and the sons of the nobles learned not only to run, to ride, to swim and to make runes or rhymes, but to be great rulers and adventurers as their forefathers had been. Alfred was a very busy King, for not only had he to receive ambassadors and counsellors, but he had to ride through the land, seeing justice done, and restoring the ruined churches and monasteries. He taught the workers in gold and artificers of all kinds, to build houses majestic and good, beyond all that had been built before. What shall I say of the cities and towns which he restored, and of the others which he built, where before there had never been any? Or of the work in gold and silver, incomparably made under his directions? Or of the halls and royal chambers wonderfully made of stone and wood by his command? Or of the royal residences built of stone, moved from their former positions and most beautifully set up in more fitting places by the King's command?
The King gave many gifts to the craftsmen whom he had gathered from all lands, men skilled in every earthly work, and he gave a portion to the wayfaring men who came to him from every nation, lying near and far, and who sought from him wealth, and even to those who sought it not. There were no clocks in those days and the King was much troubled, for he had promised to give up to God half his services. He could not equally distinguish the length of the hours by night, on account of the darkness: and oftentimes of the day, on account of storms and clouds. After long reflection on these things he at length, by a useful and shrewd invention, commanded his chaplain to supply wax in sufficient quantities. He caused the chaplain to make six candles of equal length, so that each candle might have twelve divisions marked upon it. These candles burned for twenty-four hours, a night and a day. But sometimes, from the violence of the wind, which blew through the doors and windows of the chambers or the canvas of the tents, they burned out before their time. The King then considered by what means he might shut out the wind; and so he ordered a lantern which was closed up, by the King's command, by a door made of horn. By this means, six candles lasted twenty-four hours, and when they went out others were lighted. Thus the King left behind him as he wished a memory in good works, and, after him, his son and daughter drove the Danes eastward beyond Watling Street. The northmen came back with the strong King Cnut, who conquered the whole country. Now Cnut was a great king before he took England, for he ruled Sweden and Denmark and was lord over Norway. When he was crowned King of England, he began to love this kingdom more than all his lands, and he made his home in London. He wanted to be a real English King, so he looked for the old laws of Alfred the Great and told the English people that he would rule as Alfred had done.
The King had a fine army of tall, strong soldiers, but he sent nearly all of them back to their own land and kept only three thousand house- companions for a body guard. The English people knew that he trusted them, for he could not have kept the land in order with so few soldiers, if the people had hated him. For seventeen years, there was a great peace in the land and ships could pass to and fro, carrying skins, silks, costly gems and gold, besides garments, wine, oil, ivory, with brass and copper, and tin and silver and glass and such like. When Cnut's two sons had reigned in the land, then the Saxons once more had a Saxon King.
CHAPTER VII THE BATTLE OF HASTINGS Edward the Confessor, the Saxon prince, had taken refuge in Normandy in the days when the Danish Kings ruled in England. There he learned to speak Norman French and to love Norman ways. When the Saxons chose him to be king, he brought some of his Norman friends to court with him. He was a man full of grace and devoted to the service of God. He left the rule of his kingdom to three Saxon Earls, Siward the Stout, a man who struck terror to the hearts of the Scots, Leofric of the Marsh land, wise in the things of God and men, and Godwin of Wessex. There was much trouble because there were no heirs to the throne, and the Norman chroniclers say that the King promised his crown to William, Duke of Normandy. The Saxons did not know this, and if they had they would not have crowned him; so they chose Harold, son of Godwin and brother of the Queen, to rule after Edward the Confessor. They chose Harold for he was a man after their own heart, strong and fearless, like the heroes of old. Harold had two elder brothers, but they were cruel and lawless and the people feared them. The Normans told a story of how Harold had been wrecked on the coast of Normandy, two years before this, and was taken before the Duke as a prisoner. The Duke would not let him go until he had sworn, with his hand upon the holy relics, that he would never claim the Saxon crown. When Edward died, Harold forgot this oath and the people crowned him with much rejoicing. When the news reached the Duke of
Normandy he was in his park of Quévilly, near Rouen, with many knights and squires, going forth to the chase. He had in his hand the bow, ready strung and bent for the arrow. The messenger greeted him and took him aside to tell him. Then the Duke was very angry. Oft he tied his mantle and oft he untied it again and he spoke to no man, neither dare any man speak to him. Then he bade his men cut down the trees in the great forests and build him ships to take his soldiers to England. When they were ready, there arose a great storm and for many weeks he waited by the sea shore for a fair wind and a good tide. Tostig, too, Harold's brother, became very jealous and asked for a half of the kingdom. And because Harold would not listen, Tostig went to Norway, to beg the great King Hadrada to call out his men and ships and sail for England. So the Northmen sailed up the river Humber and took York. Then, Harold and his soldiers marched to the North to fight against Tostig. When he had pitched his camp, he sent word to Tostig, King Harold, thy brother, sends thee greeting, saying that thou shalt have the whole of Northumbria or even the third of his kingdom, if thou wilt make peace with him. But, said Tostig, what shall be given to the King of Norway for his trouble? Seven feet of English ground, was the answer, or as much more as is needful, seeing that he is taller than other men. Then said the Earl, Go now and tell King Harold to get ready for battle, for never shall the Northmen say that Tostig left Hadrada, King of Norway, to join the enemy. And when Harold departed, the King of Norway asked who it was that had spoken so well. That, said Tostig, was my brother Harold. When Hadrada heard this he said, That English king was a little man, but he stood strong in his stirrups. A great fight there was, and Hadrada fought fiercely, but he was killed by an arrow. When the sun set, the Northmen turned and fled, for Tostig, too, lay dead upon the field. That night there was a great feast in the Saxon camp. As they held wassail, a messenger came riding into the camp, breathless with haste, for he had rested not day nor night in the long ride to the North. He shouted to those who stood by, The Normans— the Normans are come—they have landed at Hastings—Thy land, O
King, they will wrest from thee, if thou canst not defend it well. That night, the Saxons broke up their camp and hurried towards London. The wise men begged Harold to burn the land, that the enemy might starve, but Harold would not, for he said, How can I do harm to my own people? So they rode off to meet the Duke near Hastings. Now Harold chose his battle-field very wisely, a rising ground, for most of his soldiers were on foot and many of the Normans were on horse- back and the King knew that it was hard riding up hill. So Harold stood under the Golden Dragon of Wessex watching the enemy below. In the front of the Normans rode their minstrel, throwing his sword into the air and catching it again, as he sang of the brave deeds of those knights of old, Roland and Oliver. Fierce was the onslaught, and soon the Normans turned to flee. Then were the Saxons so eager for the spoil that they came down from their high ground to chase the enemy. When the Duke saw this, he wheeled his men in battle array and the fight began again fiercer than ever. Then the Duke ordered a great shower of arrows to be shot up into the air, so that when they fell, they pierced many a good soldier. And Harold fell, shot through the eye by an arrow. Still, the Saxons fought on, for they held it shame to escape alive from the fields whereon their leader lay slain. That night, William pitched his tent where the King's banner had waved. Then came Gyda the mother of Harold to beg Harold's body from the Duke. But he gave orders that it should be buried by the seashore, Harold guarded the cliffs when he was alive, let him guard them, now that he is dead, said William. So the King's mother and his brothers hid in the rocky west, in Tintagel, for fear of the Duke's anger. Then did William march slowly to London, burning and harrying the land, and all men feared him.
HAROLD DEFEATS AND KILLS TOSTIG AND THE KING OF NORWAY AT STAMFORD BRIDGE
A BATTLE IN THE 15TH CENTURY There is a piece of tapestry still kept at Bayeux in France, showing how England was conquered. It was probably made later than the reign of William and perhaps was intended to go round the walls of the choir of Bayeux Cathedral, for it has been measured and found to be of the right length. Though it is old and torn and faded, we have been able to learn many things from it [2]. There were few histories written in those days, for the Normans were too busy fighting for their new lands and the English were too sorrowful to tell their story.
[2] There is a copy in Reading Museum. See Guide to Bayeux Tapestry, published by Textile Department, Victoria and Albert Museum.
CHAPTER VIII THE NORMAN KINGS The strong men of the north had not bowed to William the Conqueror on the field of Hastings, and when they heard that he was crowned, they armed themselves against him. The King marched towards the north slowly, burning and harrying the land as he passed, and his path was marked by flaming villages and hayricks. When he came into Yorkshire, he laid waste the land, and for nine years not an acre was tilled beyond the Humber, and dens of wild beasts and of robbers, to the great terror of the traveller, alone were to be seen. The Saxons fled; some died of hunger by the wayside, some sold themselves as slaves, and a few hid themselves in the Fens, a great stretch of water and marsh land, in the east, dotted here and there with islands and sometimes crossed in winter on sledges. There Hereward the Wake built his camp in the swamps of Ely and there all true men gathered round him. He was bold and hardy and even William said of him, if there had been in England three such men as he, they would have driven out the Normans. The King gave orders that a causeway should be built across the Fens and he besieged the Saxons in Ely, and some said that Hereward was betrayed. But William pardoned him and sent him to Normandy to command his army. Many stories are told of his adventures. It was said that he was slain one day as he slept in an orchard, for there were many in the King's court who envied him.
The Conqueror was a wise king, and he desired to know what manner of kingdom he had conquered. He held a great council and very deep speech with his wise men about this land, how it was peopled and by what men. So he sent his clerks to every shire and commanded them to write down on a great roll all that they could find out about the country. They were to ask of the lord and of the freemen in the villages and of the monks in the monasteries these questions: How much land have you? Who gave you that land? What services do you owe the King for it? Have you paid them? How many people dwell upon your land? How many soldiers must you lend to the King if need be? How many cattle have you? Have you a mill? (if they had, they owed every third penny to the King). Have you a fish pond? (fish was a great luxury). The lords and the monks were unwilling to answer, for they knew they must pay to the King all that was due. So narrowly did the King make them seek out all this that there was not a single yard of land (shameful it is to tell, though he thought it no shame to do) nor one ox, nor one cow, nor one swine left out, that was not set down in his rolls, and all these rolls were afterwards brought to him. These records are called Domesday Book. The Kings, when they desired to get money or soldiers from the great lords and monks, turned to the Domesday Book. When the book was brought to the King, he summoned the lords and freemen to come to do him homage. These men came and they placed their hands between the King's hands and, kneeling before him, they promised to be the King's men and to follow him in time of need. Hear, my lord, said the baron, I become liege man of yours for life and limb … and I will keep faith and loyalty to you for life and death, God help me. William I made great peace in the land, and, as he was dying, he called his three sons to him, and to Robert, the eldest, he gave Normandy and to William Rufus, England. Then Henry turned
sorrowfully to his father, And what, my father, do you give to me? The King replied, I bequeath £500 to you from my treasury. Then said Henry, What shall I do with this money, having no corner of the earth I can call my own? But his father replied, My son, be content with your lot and trust heaven, Robert will have Normandy and William England. But you also in your turn will rule over the lands which are mine and you will be greater and richer than either of your brothers. Rufus ruled over England thirteen years, and he was hated by the people. Robert gave Normandy to his brother for a sum of money; and thus Henry, when Rufus was dead, became Duke of Normandy and King of England. He married a Saxon lady and there was great awe of him in the land, he made peace for man and beast.
CHAPTER IX THE NORMAN BARONS The Norman barons who came to England with William the Conqueror were much disappointed, for they had hoped to share the kingdom with him and to be great lords. But William had not given them as much land as they desired, and he had made Domesday Book so that they should render to him due service and payment in return for his gifts. The barons had not always paid that which they owed; and Henry I made a rule that all should come to his Court three times a year, to Winchester at the feast of Easter, to Westminster at Whitsuntide and to Gloucester at Mid-winter, when he wore his crown, and then they should do homage and pay their taxes. To this court came the officers of the household, and the King appointed a Bishop to receive the money and priests to keep the accounts, since there were few among the nobles or citizens who could read, write and add figures. The money was counted out on a chequered table, and so the court came to be called the Exchequer. The barons could not easily cheat the King; for, when their money had been counted out upon the table, some of it was melted on the furnace, lest it should contain base metal, and it was weighed in the balances, lest the coins should have been clipped. Then Domesday Book was searched and the priests read out what sum was due to the King from this lord. When the Chancellor was satisfied, a tally was handed to the baron. This was a willow or hazel stick, shaped something like the blade of a knife, about an inch thick. Notches were cut in it to show the amount
paid and the halfpennies were marked by small holes. The tally was then split down the middle through the notches, and the baron took one half so that he might show it to the Chancellor when he came to court to pay again, and the Chancellor kept the other half to prove that the baron was not cheating. Thus the King kept his barons in order and there was peace in the land. Now Henry I had an only son, and to him he gave a ship, a better one than which there did not seem to be in the fleet, but as he was sailing from Normandy to England, it struck upon a rock and all perished, save only a butcher, who was found in the morning clinging to a plank. When the King heard the news, he was in great distress; for no woman had yet ruled in England and his daughter Matilda was married to a French Count, whom all the Normans hated for his fierce temper and overbearing ways. The King, nevertheless, made them swear to put her on the throne, but, when he died, the barons chose her cousin, Stephen, for he was a mild man, soft and good, and did no justice. Stephen quarrelled with the Chancellor and closed the Court of Exchequer where the barons had paid their dues, and he let the barons build castles and coin their own money. When he was in need of soldiers, he hired foreign ruffians, and because he could not pay them, he let them loose upon the land to plunder: thus he undid all his cousins had done. The barons forswore themselves and broke their troth, for every nobleman made him a castle and held it against the King and filled the land full of castles. They put the wretched country folk to sore toil with their castle-building; and, when the castles were made, they filled them with devils and evil men. Then they took all those that they deemed had any goods, both by night and day, men and women alike, and put them in prison to get their gold and silver, and tortured them with tortures unspeakable. Many thousands they slew with hunger. I
cannot nor may not tell all the horrors and all the tortures that they laid on wretched men in the land. And this lasted nineteen winters, while Stephen was King, and ever it was worse and worse. They laid taxes on the villages continually, and, when the wretched folk had no more to give them, they robbed and burned all the villages, so that thou mightest easily fare a whole day's journey and shouldst never find a man living in a village nor a land tilled. Then was corn dear, and flesh and cheese, and there was none in the land. If two or three men came riding to a village, all the village folk fled before them, deeming them to be robbers. Wheresoever men tilled, the earth bore no corn, for the land was fordone with such deeds, and they said openly that Christ and His Saints slept. Such, and more than we can say, we suffered nineteen winters for our sins. Then Stephen made a treaty with Matilda's son Henry and promised him the crown of England; for Henry was already a great prince, holding more lands than the monarch of France. Moreover, he was valiant in battle, strong in the Council chamber and never weary. The French King said of him, Henry is now in England, now in Ireland, now in Normandy, he may be rather said to fly than go by horse or boat. Henry II could ride all night and, if need were, sleep in the saddle. His legs were bruised and livid with riding. He was given beyond measure to the pleasures of hunting; and he would start off the first thing in the morning on a fleet horse and now traversing the woodland glades, now plunging into the forest itself, now crossing the ridges of the hills, would in this manner pass day after day in unwearied exertion; and when, in the evening, he reached home, he was rarely seen to sit down whether before or after supper. In spite of all the fatigue he had undergone, he would keep the whole court standing. This tireless ruler, before he became King, had restored order in England, for he commanded the hired soldiers to be gone immediately, and they went as they had come like a flight of locusts. He destroyed
more than a thousand castles, and those that were well built he kept for himself. All folk loved him, for he did good justice. He opened the Court of Exchequer, so that the Barons were forced to pay all they owed Stephen for the nineteen years of his reign. He visited all the courts of justice in the land, and no man durst do evil, for none knew where the King might be. He appointed judges to travel round the country and to sit at Westminster and hear complaints, for many had sought the King in vain, so swiftly did he travel from place to place. Thus the barons were made to fear the King and rule justly.
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Web Age Information Management 17th International Conference WAIM 2016 Nanchang China June 3 5 2016 Proceedings Part I 1st Edition Bin Cui

  • 1.
    Web Age InformationManagement 17th International Conference WAIM 2016 Nanchang China June 3 5 2016 Proceedings Part I 1st Edition Bin Cui pdf download https://textbookfull.com/product/web-age-information- management-17th-international-conference-waim-2016-nanchang- china-june-3-5-2016-proceedings-part-i-1st-edition-bin-cui/ Download more ebook from https://textbookfull.com
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    Artificial Intelligence andSoft Computing 15th International Conference ICAISC 2016 Zakopane Poland June 12 16 2016 Proceedings Part I 1st Edition Leszek Rutkowski https://textbookfull.com/product/artificial-intelligence-and- soft-computing-15th-international-conference- icaisc-2016-zakopane-poland-june-12-16-2016-proceedings- part-i-1st-edition-leszek-rutkowski/ Computational Linguistics and Intelligent Text Processing 17th International Conference CICLing 2016 Konya Turkey April 3 9 2016 Revised Selected Papers Part I Alexander Gelbukh https://textbookfull.com/product/computational-linguistics-and- intelligent-text-processing-17th-international-conference- cicling-2016-konya-turkey-april-3-9-2016-revised-selected-papers- part-i-alexander-gelbukh/ Advances in Multimedia Information Processing PCM 2016 17th Pacific Rim Conference on Multimedia Xi an China September 15 16 2016 Proceedings Part II 1st Edition Enqing Chen https://textbookfull.com/product/advances-in-multimedia- information-processing-pcm-2016-17th-pacific-rim-conference-on- multimedia-xi-an-china-september-15-16-2016-proceedings-part- ii-1st-edition-enqing-chen/ Engineering Applications of Neural Networks 17th International Conference EANN 2016 Aberdeen UK September 2 5 2016 Proceedings 1st Edition Chrisina Jayne https://textbookfull.com/product/engineering-applications-of- neural-networks-17th-international-conference-eann-2016-aberdeen- uk-september-2-5-2016-proceedings-1st-edition-chrisina-jayne/ HCI International 2016 Posters Extended Abstracts 18th International Conference HCI International 2016 Toronto Canada July 17 22 2016 Proceedings Part I 1st Edition Constantine Stephanidis (Eds.) https://textbookfull.com/product/hci-international-2016-posters- extended-abstracts-18th-international-conference-hci- international-2016-toronto-canada-july-17-22-2016-proceedings-
  • 4.
    Bin Cui ·Nan Zhang · Jianliang Xu Xiang Lian · Dexi Liu (Eds.) 123 LNCS 9658 17th International Conference, WAIM 2016 Nanchang, China, June 3–5, 2016 Proceedings, Part I Web-Age Information Management
  • 5.
    Lecture Notes inComputer Science 9658 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, Lancaster, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Zürich, Switzerland John C. Mitchell Stanford University, Stanford, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Dortmund, Germany Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbrücken, Germany
  • 6.
    More information aboutthis series at http://www.springer.com/series/7409
  • 7.
    Bin Cui •Nan Zhang • Jianliang Xu Xiang Lian • Dexi Liu (Eds.) Web-Age Information Management 17th International Conference, WAIM 2016 Nanchang, China, June 3–5, 2016 Proceedings, Part I 123
  • 8.
    Editors Bin Cui Peking University Beijing China NanZhang The George Washington University Washington, D.C. USA Jianliang Xu Hong Kong Baptist University Kowloon Tong, Hong Kong SAR China Xiang Lian University of Texas Rio Grande Valley Edinburg, TX USA Dexi Liu Jiangxi University of Finance and Economics Nanchang China ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-319-39936-2 ISBN 978-3-319-39937-9 (eBook) DOI 10.1007/978-3-319-39937-9 Library of Congress Control Number: 2016940123 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
  • 9.
    Preface This volume containsthe proceedings of the 17th International Conference on Web- Age Information Management (WAIM), held during June 3–5, 2016, in Nanchang, Jiangxi, China. As a flagship conference in the Asia-Pacific region focusing on the research, development, and applications of Web information management, its success has been witnessed through the previous conference series that were held in Shanghai (2000), Xi’an (2001), Beijing (2002), Chengdu (2003), Dalian (2004), Hangzhou (2005), Hong Kong (2006), Huangshan (2007), Zhangjiajie (2008), Suzhou (2009), Jiuzhaigou (2010), Wuhan (2011), Harbin (2012), Beidahe (2013), Macau (2014), and Qingdao (2015). With the fast development of Web-related technologies, we expect that WAIM will become an increasingly popular forum to bring together outstanding researchers in this field from all over the world. This high-quality program would not have been possible without the authors who chose WAIM for disseminating their contributions. Out of 249 submissions to the research track and 17 to the demonstration track, the conference accepted 80 research papers and eight demonstrations. The contributed papers address a wide range of topics, such as big data analytics, data mining, query processing and optimization, security, privacy, trust, recommender systems, spatial databases, information retrieval and Web search, information extraction and integration, data and information quality, distributed and cloud computing, among others. The technical program of WAIM 2016 also included two keynote talks by Profs. Beng Chin Ooi (National University of Singapore) and Yanchun Zhang (Victoria University, Australia), as well as three talks in the Distinguished Young Lecturer Series by Profs. Tingjian Ge (University of Massachusetts at Lowell), Hua Lu (Aalborg University), and Haibo Hu (Hong Kong Polytechnic University). We are immensely grateful to these distinguished guests for their invaluable contributions to the confer- ence program. A conference like WAIM can only succeed as a team effort. We are deeply thankful to the Program Committee members and the reviewers for their invaluable efforts. Special thanks to the local Organizing Committee headed by Guoqiong Liao and Xiaobing Mao. Many thanks also go to our workshop co-chairs (Shaoxu Song and Yongxin Tong), proceedings co-chairs (Xiang Lian and Dexi Liu), DYL co-chairs (Hong Gao and Weiyi Meng), demo co-chairs (Xiping Liu and Yi Yu), publicity co- chairs (Ye Yuan, Hua Lu, and Chengkai Li), registration chair (Yong Yang), and finance chair (Bo Shen). Last but not least, we wish to express our gratitude for the hard
  • 10.
    work of ourwebmaster (Bo Yang), and for our sponsors who generously supported the smooth running of our conference. We hope you enjoy the proceedings WAIM 2016! June 2016 Zhanhuai Li Sang Kyun Cha Changxuan Wan Bin Cui Nan Zhang Jianliang Xu VI Preface
  • 11.
    Organization Organizing Committee Honor Chair QiaoWang Jiangxi University of Finance and Economics, China General Co-chairs Zhanhuai Li Northwestern Polytechnical University, China Sang Kyun Cha Seoul National University, Korea Changxuan Wan Jiangxi University of Finance and Economics, China PC Co-chairs Bin Cui Peking University, China Nan Zhang George Washington University, USA Jianliang Xu Hong Kong Baptist University, SAR China Workshop Co-chairs Shaoxu Song Tsinghua University, China Yongxin Tong Beihang University, China Proceedings Co-chairs Xiang Lian The University of Texas Rio Grande Valley, USA Dexi Liu Jiangxi University of Finance and Economics, China DYL Series Co-chairs (Distinguished Young Lecturer) Hong Gao Harbin Institute of Technology, China Weiyi Meng SUNY Binghamton, USA Demo Co-chairs Xiping Liu Jiangxi University of Finance and Economics, China Yi Yu National Institute of Informatics, Japan
  • 12.
    Publicity Co-chairs Ye YuanNortheastern University, China Hua Lu Aalborg University, Denmark Chengkai Li The University of Texas at Arlington, USA Local Organization Co-chairs Xiaobing Mao Jiangxi University of Finance and Economics, China Guoqiong Liao Jiangxi University of Finance and Economics, China Registration Chair Yong Yang Jiangxi University of Finance and Economics, China Finance Chair Bo Shen Jiangxi University of Finance and Economics, China Web Chair Bo Yang Jiangxi University of Finance and Economics, China Steering Committee Liaison Weiyi Meng SUNY Binghamton, USA CCF DBS Liaison Xiaofeng Meng Renmin University of China, China Program Committee Alex Thomo University of Victoria, Canada Anirban Mondal Xerox Research Centre India, India Baihua Zheng Singapore Management University, Singapore Baoning Niu Taiyuan University of Technology, China Byron Choi Hong Kong Baptist University, SAR China Carson Leung University of Manitoba, Canada Ce Zhang Stanford University, USA Chengkai Li The University of Texas at Arlington, USA Chih-Hua Tai National Taipei University, China Cuiping Li Renmin University of China, China David Cheung The University of Hong Kong, SAR China Dejing Dou University of Oregon, USA De-Nian Yang Academia Sinica, Taiwan, China VIII Organization
  • 13.
    Dongxiang Zhang NationalUniversity of Singapore, Singapore Feida Zhu Singapore Management University, Singapore Feifei Li University of Utah, USA Fuzhen Zhuang ICT, Chinese Academy of Sciences, China Gang Chen Zhejiang University, China Gao Cong Nanyang Technological University, Singapore Giovanna Guerrini Università di Genova, Italy Guohui Li Huazhong University of Science and Technology, China Guoliang Li Tsinghua University, China Guoqiong Liao Jiangxi University of Finance and Economics, China Haibo Hu Hong Kong Polytechnic University, SAR China Hailong Sun Beihang University, China Hiroaki Ohshima Kyoto University, Japan Hongyan Liu Tsinghua University, China Hongzhi Wang Harbin Institue of Technology, China Hongzhi Yin The University of Queensland, Australia Hua Lu Aalborg University, Denmark Jae-Gil Lee Korea Advanced Institute of Science and Technology, Korea Jeffrey Xu Yu Chinese University of Hong Kong, SAR China Jiaheng Lu Renmin University of China Jianbin Qin The University of New South Wales, Australia Jianbin Huang Xidian University, China Jiannan Wang University of Berkerley, USA Jie Shao University of Electronic Science and Technology of China, China Jinchuan Chen Renmin University of China, China Jingfeng Guo Yanshan University, China Jiun-Long Huang National Chiao Tung University, Taiwan, China Jizhou Luo Harbin Institue of Technology, China Ju Fan National University of Singapore, Singapore Junfeng Zhou Yanshan University, China Junjie Yao East China Normal University, China Ke Yi Hong Kong University of Science and Technology, SAR China Kun Ren Yale University, USA Kyuseok Shim Seoul National University, South Korea Lei Zou Peking University, China Leong Hou U University of Macau, SAR China Lianghuai Yang Zhejiang University of Technology, China Lidan Shou Zhejiang University, China Lili Jiang Max Planck Institute for Informatics, Germany Ling Chen University of Technology, Sydney, Australia Luke Huan University of Kansas, USA Man Lung Yiu Hong Kong Polytechnic University, SAR China Muhammad Cheema Monash University, Australia Organization IX
  • 14.
    Peiquan Jin Universityof Science and Technology of China, China Peng Wang Fudan University, China Qi Liu University of Science and Technology of China, China Qiang Wei Tsinghua University, China Qingzhong Li Shandong University, China Qinmin Hu East China Normal University, China Quan Zou Xiamen University, China Richong Zhang Beihang University, China Rui Zhang The University of Melbourne, Australia Rui Chen Samsung Research America, USA Saravanan Thirumuruganathan Qatar Computing Research Institute, Qatar Senjuti Basu Roy University of Washington, USA Shengli Wu Jiangsu University, China Shimin Chen Chinese Academy of Sciences, China Shinsuke Nakajima Kyoto Sangyo University, Japan Shuai Ma Beihang University, China Sourav Bhowmick National Taiwan University, China Takahiro Hara Osaka University, Japan Taketoshi Ushiama Kyushu University, Japan Tingjian Ge University of Massachusetts Lowell, USA Wang-Chien Lee Penn State University, USA Wei Wang University of New South Wales, Australia Weiwei Sun Fudan University, China Weiwei Ni Southeast University, China Wen-Chih Peng National Chiao Tung University, Taiwan, China Wenjie Zhang University of New South Wales, Australia Wolf-Tilo Balke TU-Braunschweig, Germany Wookey Lee Inha University, Korea Xiang Lian The University of Texas Rio Grande Valley, USA Xiangliang Zhang King Abdullah University of Science and Technology, Saudi Arabia Xiaochun Yang Northeast University, China Xiaofeng Meng Renmin University of China, China Xiaohui Yu Shandong University, China Xiaokui Xiao Nanyang Technological University, Singapore Xifeng Yan University of California at Santa Barbara, USA Xin Lin East China Normal University, China Xin Cao Queen’s University Belfast, UK Xin Wang Tianjin University, China Xingquan Zhu Florida Atlantic University, USA Xuanjing Huang Fudan University, China Yafei Li Henan University of Economics and Law, China Yang Liu Shandong University, China Yanghua Xiao Fudan University, China Yang-Sae Moon Kangwon National University, Korea X Organization
  • 15.
    Yaokai Feng KyushuUniversity, Japan Yi Zhuang Zhejiang Gongshang University, China Yijie Wang National University of Defense Technology, China Yin Yang Hamad Bin Khalifa University, Qatar Ying Zhao Tsinghua University, China Yinghui Wu Washington State University, USA Yong Zhang Tsinghua University, China Yueguo Chen Renmin University of China, China Yunjun Gao Zhejiang University, China Zhaonian Zou Harbin Institue of Technology, China Zhenjie Zhang Advanced Digital Sciences Center, Singapore Zhifeng Bao RMIT University, Australia Zhiguo Gong University of Macau, SAR China Zhiyong Peng Wuhan University, China Zhoujun Li Beihang University, China Organization XI
  • 16.
    Contents – PartI Data Mining More Efficient Algorithm for Mining Frequent Patterns with Multiple Minimum Supports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Wensheng Gan, Jerry Chun-Wei Lin, Philippe Fournier-Viger, and Han-Chieh Chao Efficient Mining of Uncertain Data for High-Utility Itemsets . . . . . . . . . . . . 17 Jerry Chun-Wei Lin, Wensheng Gan, Philippe Fournier-Viger, Tzung-Pei Hong, and Vincent S. Tseng An Improved HMM Model for Sensing Data Predicting in WSN . . . . . . . . . 31 Zeyu Zhang, Bailong Deng, Siding Chen, and Li Li eXtreme Gradient Boosting for Identifying Individual Users Across Different Digital Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Rongwei Song, Siding Chen, Bailong Deng, and Li Li Two-Phase Mining for Frequent Closed Episodes . . . . . . . . . . . . . . . . . . . . 55 Guoqiong Liao, Xiaoting Yang, Sihong Xie, Philip S. Yu, and Changxuan Wan Effectively Updating High Utility Co-location Patterns in Evolving Spatial Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Xiaoxuan Wang, Lizhen Wang, Junli Lu, and Lihua Zhou Mining Top-k Distinguishing Sequential Patterns with Flexible Gap Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 Chao Gao, Lei Duan, Guozhu Dong, Haiqing Zhang, Hao Yang, and Changjie Tang A Novel Chinese Text Mining Method for E-Commerce Review Spam Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Xiu Li and Xinwei Yan Spatial and Temporal Databases Retrieving Routes of Interest Over Road Networks . . . . . . . . . . . . . . . . . . . 109 Wengen Li, Jiannong Cao, Jihong Guan, Man Lung Yiu, and Shuigeng Zhou
  • 17.
    Semantic-Aware Trajectory Compressionwith Urban Road Network . . . . . . . 124 Na Ta, Guoliang Li, Bole Chen, and Jianhua Feng Discovering Underground Roads from Trajectories Without Road Network . . . 137 Qiuge Song, Jiali Mao, and Cheqing Jin Ridesharing Recommendation: Whether and Where Should I Wait?. . . . . . . . 151 Chengcheng Dai Keyword-aware Optimal Location Query in Road Network . . . . . . . . . . . . . 164 Jinling Bao, Xingshan Liu, Rui Zhou, and Bin Wang Point-of-Interest Recommendations by Unifying Multiple Correlations. . . . . . 178 Ce Cheng, Jiajin Huang, and Ning Zhong Top-k Team Recommendation in Spatial Crowdsourcing . . . . . . . . . . . . . . . 191 Dawei Gao, Yongxin Tong, Jieying She, Tianshu Song, Lei Chen, and Ke Xu Explicable Location Prediction Based on Preference Tensor Model . . . . . . . . 205 Duoduo Zhang, Ning Yang, and Yuchi Ma Recommender Systems Random Partition Factorization Machines for Context-Aware Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Shaoqing Wang, Cuilan Du, Kankan Zhao, Cuiping Li, Yangxi Li, Yang Zheng, Zheng Wang, and Hong Chen A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 Penghua Yu, Lanfen Lin, and Yuangang Yao RankMBPR: Rank-Aware Mutual Bayesian Personalized Ranking for Item Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 Lu Yu, Ge Zhou, Chuxu Zhang, Junming Huang, Chuang Liu, and Zi-Ke Zhang Unsupervised Expert Finding in Social Network for Personalized Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Junmei Ding, Yan Chen, Xin Li, Guiquan Liu, Aili Shen, and Xiangfu Meng An Approach for Clothing Recommendation Based on Multiple Image Attributes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Dandan Sha, Daling Wang, Xiangmin Zhou, Shi Feng, Yifei Zhang, and Ge Yu XIV Contents – Part I
  • 18.
    SocialFM: A SocialRecommender System with Factorization Machines . . . . 286 Juming Zhou, Dong Wang, Yue Ding, and Litian Yin Identifying Linked Data Datasets for sameAs Interlinking Using Recommendation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 Haichi Liu, Ting Wang, Jintao Tang, Hong Ning, Dengping Wei, Songxian Xie, and Peilei Liu Query-Biased Multi-document Abstractive Summarization via Submodular Maximization Using Event Guidance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 Rui Sun, Zhenchao Wang, Yafeng Ren, and Donghong Ji Graph Data Management Inferring Diffusion Network on Incomplete Cascade Data . . . . . . . . . . . . . . 325 Peng Dou, Sizhen Du, and Guojie Song Anchor Link Prediction Using Topological Information in Social Networks . . . 338 Shuo Feng, Derong Shen, Tiezheng Nie, Yue Kou, and Ge Yu Collaborative Partitioning for Multiple Social Networks with Anchor Nodes . . . 353 Fenglan Li, Anming Ji, Songchang Jin, Shuqiang Yang, and Qiang Liu A General Framework for Graph Matching and Its Application in Ontology Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365 Yuda Zang, Jianyong Wang, and Xuan Zhu Internet Traffic Analysis in a Large University Town: A Graphical and Clustering Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 Weitao Weng, Kai Lei, Kuai Xu, Xiaoyou Liu, and Tao Sun Conceptual Sentence Embeddings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 Yashen Wang, Heyan Huang, Chong Feng, Qiang Zhou, and Jiahui Gu Inferring Social Roles of Mobile Users Based on Communication Behaviors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 Yipeng Chen, Hongyan Li, Jinbo Zhang, and Gaoshan Miao Sparse Topical Coding with Sparse Groups . . . . . . . . . . . . . . . . . . . . . . . . 415 Min Peng, Qianqian Xie, Jiajia Huang, Jiahui Zhu, Shuang Ouyang, Jimin Huang, and Gang Tian Information Retrieval Differential Evolution-Based Fusion for Results Diversification of Web Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429 Chunlin Xu, Chunlan Huang, and Shengli Wu Contents – Part I XV
  • 19.
    BMF: An IndexingStructure to Support Multi-element Check . . . . . . . . . . . 441 Chenyang Xu and Weixiong Rao Efficient Unique Column Combinations Discovery Based on Data Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454 Chao Wang, Shupeng Han, Xiangrui Cai, Haiwei Zhang, and Yanlong Wen Event Related Document Retrieval Based on Bipartite Graph . . . . . . . . . . . . 467 Wenjing Yang, Rui Li, Peng Li, Meilin Zhou, and Bin Wang SPedia: A Semantics Based Repository of Scientific Publications Data . . . . . 479 Muhammad Ahtisham Aslam and Naif Radi Aljohani A Set-Based Training Query Classification Approach for Twitter Search . . . . 491 Qingli Ma, Ben He, Jungang Xu, and Bin Wang NBLucene: Flexible and Efficient Open Source Search Engine . . . . . . . . . . . 504 Zhaohua Zhang, Benjun Ye, Jiayi Huang, Rebecca Stones, Gang Wang, and Xiaoguang Liu Context-Aware Entity Summarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517 Jihong Yan, Yanhua Wang, Ming Gao, and Aoying Zhou Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 XVI Contents – Part I
  • 20.
    Contents – PartII Privacy and Trust Detecting Anomalous Ratings Using Matrix Factorization for Recommender Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Zhihai Yang, Zhongmin Cai, and Xinyuan Chen A Novel Spatial Cloaking Scheme Using Hierarchical Hilbert Curve for Location-Based Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Ningning Cui, Xiaochun Yang, and Bin Wang Efficient Privacy-Preserving Content-Based Image Retrieval in the Cloud . . . 28 Kai Huang, Ming Xu, Shaojing Fu, and Dongsheng Wang Preserving the d-Reachability When Anonymizing Social Networks . . . . . . . 40 Xiangyu Liu, Jiajia Li, Dahai Zhou, Yunzhe An, and Xiufeng Xia Personalized Location Anonymity - A Kernel Density Estimation Approach . . . 52 Dapeng Zhao, Jiansong Ma, Xiaoling Wang, and Xiuxia Tian Detecting Data-model-oriented Anomalies in Parallel Business Process . . . . . 65 Ning Yin, Shanshan Wang, Hongyan Li, and Lilue Fan Learning User Credibility on Aspects from Review Texts . . . . . . . . . . . . . . 78 Yifan Gao, Yuming Li, Yanhong Pan, Jiali Mao, and Rong Zhang Detecting Anomaly in Traffic Flow from Road Similarity Analysis . . . . . . . . 92 Xinran Liu, Xingwu Liu, Yuanhong Wang, Juhua Pu, and Xiangliang Zhang Query Processing and Optimization PACOKS: Progressive Ant-Colony-Optimization-Based Keyword Search over Relational Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 Ziyu Lin, Qian Xue, and Yongxuan Lai Enhanced Query Classification with Millions of Fine-Grained Topics . . . . . . 120 Qi Ye, Feng Wang, Bo Li, and Zhimin Liu A Hybrid Machine-Crowdsourcing Approach for Web Table Matching and Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Chunhua Li, Pengpeng Zhao, Victor S. Sheng, Zhixu Li, Guanfeng Liu, Jian Wu, and Zhiming Cui
  • 21.
    An Update Methodfor Shortest Path Caching with Burst Paths Based on Sliding Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Xiaohua Li, Ning Wang, Kanggui Peng, Xiaochun Yang, and Ge Yu Low Overhead Log Replication for Main Memory Database System . . . . . . . 159 Jinwei Guo, Chendong Zhang, Peng Cai, Minqi Zhou, and Aoying Zhou Diversification of Keyword Query Result Patterns. . . . . . . . . . . . . . . . . . . . 171 Cem Aksoy, Ananya Dass, Dimitri Theodoratos, and Xiaoying Wu Efficient Approximate Substring Matching in Compressed String . . . . . . . . . 184 Yutong Han, Bin Wang, and Xiaochun Yang Top-K Similarity Search for Query-By-Humming . . . . . . . . . . . . . . . . . . . . 198 Peipei Wang, Bin Wang, and Shiying Luo Social Media Restricted Boltzmann Machines for Retweeting Behaviours Prediction. . . . . . 213 Xiang Li, Lijuan Xie, Yong Tan, and Qiuli Tong Cross-Collection Emotion Tagging for Online News . . . . . . . . . . . . . . . . . . 225 Li Yu, Xue Zhao, Chao Wang, Haiwei Zhang, and Ying Zhang Online News Emotion Prediction with Bidirectional LSTM . . . . . . . . . . . . . 238 Xue Zhao, Chao Wang, Zhifan Yang, Ying Zhang, and Xiaojie Yuan Learning for Search Results Diversification in Twitter . . . . . . . . . . . . . . . . . 251 Ying Wang, Zhunchen Luo, and Yang Yu Adjustable Time-Window-Based Event Detection on Twitter . . . . . . . . . . . . 265 Qinyi Wang, Jieying She, Tianshu Song, Yongxin Tong, Lei Chen, and Ke Xu User-IBTM: An Online Framework for Hashtag Suggestion in Twitter . . . . . 279 Jia Li and Hua Xu Unifying User and Message Clustering Information for Retweeting Behavior Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291 Bo Jiang, Jiguang Liang, Ying Sha, Lihong Wang, Zhixin Kuang, Rui Li, and Peng Li KPCA-WT: An Efficient Framework for High Quality Microblog Extraction in Time-Frequency Domain. . . . . . . . . . . . . . . . . . . . . . . . . . . . 304 Min Peng, Xinyuan Dai, Kai Zhang, Guanyin Zeng, Jiahui Zhu, Shuang Ouyang, Qianqian Xie, and Gang Tian XVIII Contents – Part II
  • 22.
    Big Data Analytics ActiveLearning Method for Constraint-Based Clustering Algorithms. . . . . . . 319 Lijun Cai, Tinghao Yu, Tingqin He, Lei Chen, and Meiqi Lin An Effective Cluster Assignment Strategy for Large Time Series Data. . . . . . 330 Damir Mirzanurov, Waqas Nawaz, JooYoung Lee, and Qiang Qu AdaWIRL: A Novel Bayesian Ranking Approach for Personal Big-Hit Paper Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Chuxu Zhang, Lu Yu, Jie Lu, Tao Zhou, and Zi-Ke Zhang Detecting Live Events by Mining Textual and Spatial-Temporal Features from Microblogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 356 Zhejun Zheng, Beihong Jin, Yanling Cui, and Qiang Ji A Label Correlation Based Weighting Feature Selection Approach for Multi-label Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Lu Liu, Jing Zhang, Peipei Li, Yuhong Zhang, and Xuegang Hu Valuable Group Trajectory Pattern Mining Directed by Adaptable Value Measuring Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380 Xinyu Huang, Tengjiao Wang, Shun Li, and Wei Chen DualPOS: A Semi-supervised Attribute Selection Approach for Symbolic Data Based on Rough Set Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 392 Jianhua Dai, Huifeng Han, Hu Hu, Qinghua Hu, Jinghong Zhang, and Wentao Wang Semi-supervised Clustering Based on Artificial Bee Colony Algorithm with Kernel Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 Jianhua Dai, Huifeng Han, Hu Hu, Qinghua Hu, Bingjie Wei, and Yuejun Yan Distributed and Cloud Computing HMNRS: A Hierarchical Multi-source Name Resolution Service for the Industrial Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 Yang Liu, Guoqiang Fu, and Xinchi Li Optimizing Replica Exchange Strategy for Load Balancing in Multienant Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 Teng Liu, Qingzhong Li, Lanju Kong, Lei Liu, and Lizhen Cui ERPC: An Edge-Resources Based Framework to Reduce Bandwidth Cost in the Personal Cloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444 Shaoduo Gan, Jie Yu, Xiaoling Li, Jun Ma, Lei Luo, Qingbo Wu, and Shasha Li Contents – Part II XIX
  • 23.
    Multidimensional Similarity JoinUsing MapReduce . . . . . . . . . . . . . . . . . . 457 Ye Li, Jian Wang, and Leong Hou U Real-Time Logo Recognition from Live Video Streams Using an Elastic Cloud Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 Jianbing Ding, Hongyang Chao, and Mansheng Yang Profit Based Two-Step Job Scheduling in Clouds . . . . . . . . . . . . . . . . . . . . 481 Shuo Zhang, Li Pan, Shijun Liu, Lei Wu, and Xiangxu Meng A Join Optimization Method for CPU/MIC Heterogeneous Systems . . . . . . . 493 Kailai Zhou, Hong Chen, Hui Sun, Cuiping Li, and Tianzhen Wu GFSF: A Novel Similarity Join Method Based on Frequency Vector . . . . . . . 506 Ziyu Lin, Daowen Luo, and Yongxuan Lai Demo Papers SHMS: A Smart Phone Self-health Management System Using Data Mining. . . 521 Chuanhua Xu, Jia Zhu, Zhixu Li, Jing Xiao, Changqin Huang, and Yong Tang MVUC: An Interactive System for Mining and Visualizing Urban Co-locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524 Xiao Wang, Hongmei Chen, and Qing Xiao SNExtractor: A Prototype for Extracting Semantic Networks from Web Documents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Chi Zhang, Yanhua Wang, Chengyu Wang, Wenliang Cheng, and Xiaofeng He Crowd-PANDA: Using Crowdsourcing Method for Academic Knowledge Acquisition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531 Zhaoan Dong, Jiaheng Lu, and Tok Wang Ling LPSMon: A Stream-Based Live Public Sentiment Monitoring System . . . . . . 534 Kun Ma, Zijie Tang, Jialin Zhong, and Bo Yang DPBT: A System for Detecting Pacemakers in Burst Topics. . . . . . . . . . . . . 537 Guozhong Dong, Wu Yang, Feida Zhu, and Wei Wang CEQA - An Open Source Chinese Question Answer System Based on Linked Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540 Zeyu Du, Yan Yang, Qinming Hu, and Liang He XX Contents – Part II
  • 24.
    OSSRec:An Open SourceSoftware Recommendation System Based on Wisdom of Crowds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 Mengwen Chen, Gang Yin, Chenxi Song, Tao Wang, Cheng Yang, and Huaimin Wang Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Contents – Part II XXI
  • 25.
  • 26.
    More Efficient Algorithmfor Mining Frequent Patterns with Multiple Minimum Supports Wensheng Gan1 , Jerry Chun-Wei Lin1(B) , Philippe Fournier-Viger2 , and Han-Chieh Chao1,3 1 School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China wsgan001@gmail.com, jerrylin@ieee.org 2 School of Natural Sciences and Humanities, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China philfv@hitsz.edu.cn 3 Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien County, Taiwan hcc@ndhu.edu.tw Abstract. Frequent pattern mining (FPM) is an important data mining task, having numerous applications. However, an important limitation of traditional FPM algorithms, is that they rely on a single minimum sup- port threshold to identify frequent patterns (FPs). As a solution, several algorithms have been proposed to mine FPs using multiple minimum sup- ports. Nevertheless, a crucial problem is that these algorithms generally consume a large amount of memory and have long execution times. In this paper, we address this issue by introducing a novel algorithm named efficient discovery of Frequent Patterns with Multiple minimum supports from the Enumeration-tree (FP-ME). The proposed algorithm discovers FPs using a novel Set-Enumeration-tree structure with Multiple mini- mum supports (ME-tree), and employs a novel sorted downward closure (SDC) property of FPs with multiple minimum supports. The proposed algorithm directly discovers FPs from the ME-tree without generating candidates. Furthermore, an improved algorithms, named FP-MEDiffSet, is also proposed based on the DiffSet concept, to further increase mining performance. Substantial experiments on real-life datasets show that the proposed approaches not only avoid the “rare item problem”, but also efficiently and effectively discover the complete set of FPs in transac- tional databases. Keywords: Frequent patterns · Multiple minimum supports · Sorted downward closure property · Set-enumeration-tree · DiffSet 1 Introduction In the process of knowledge discovery in database (KDD) [2,3], many approaches have been proposed to discover more useful and invaluable information from c Springer International Publishing Switzerland 2016 B. Cui et al. (Eds.): WAIM 2016, Part I, LNCS 9658, pp. 3–16, 2016. DOI: 10.1007/978-3-319-39937-9 1
  • 27.
    4 W. Ganet al. huge databases. Among them, frequent pattern mining (FPM) and association rule mining (ARM) [2–4] have been extensively studied. Most studies in FPM or ARM focus on developing efficient algorithms to mine frequent patterns (FPs) in a transactional database, in which the occurrence frequency of each item- set is no less than a specified number of customer transactions w.r.t. the user- specified minimum support threshold (called minsup). However, they suffer from an important limitation, which is to utilize a single minimum support threshold as the measure to discover the complete set of FPs. Using a single threshold to assess the occurrence frequencies of all items in a database is inadequate since each item is different and should not all be treated the same. Hence, it is hard to carry out a fair measurement of the frequencies of itemsets using a single minimum support when mining FPs. For market basket analysis, a traditional FPM algorithm may discover many itemsets that are frequent but generate a low profit and fail to dis- cover itemsets that are rare but generate a high profit. For example, clothes i.e., {shirt, tie, trousers, suits} occur much more frequent than {diamond} in a supermarket, and both have positive contribution to increase the profit amount. If the value of minsup is set too high, though the rule {shirt, tie ⇒ trousers} can be found, we would never find the rule {shirt, tie ⇒ diamond}. To find the second rule, we need to set the minsup very low. However, this will cause lots of meaningless rules to be found at the same time. This is the co-called “rare item problem” [7]. To address this issue, the problem of frequent pattern min- ing with multiple minimum supports (FP-MMS) has been studied. Liu et al. [7] introduced the problem of FP-MMS and proposed the MSApriori algorithm by extending the level-wise Apriori algorithm. The goal of FP-MMS is to discover the useful set of itemsets that are “frequent” for the users. It allows the users to free set multiple minimum support thresholds instead of an uniform mini- mum support threshold to reflect different natures and frequencies of all items. Some approaches have been designed for the mining task of FP-MMS, such as MSApriori [7], CFP-growth [11], CFP-growth++ [10], etc. The state-of-the-art CFP-growth++ was proposed by extending the FP-growth [4] approach to mine FPs from a condensed CFP-tree structure. However, the mining efficiency of them is still a major problem. Since the previous studies of FP-MMS still suffer the time-consuming and memory usage problems, it is thus quite challenging and critically important to design an efficient algorithm to solve this problem. In this paper, we propose a novel mining framework named mining frequent patterns from the Set-enumeration-tree with multiple minimum supports to address this important research gap. Major contributions are summarized as follows: – Being different from the Apriori-like and FP-growth-based approaches, we propose a novel algorithm for directly extracting FPs with multiple minimum supports from the Set-enumeration-tree (abbreviated as FP-ME). It allows the user to specify multiple minimum support thresholds to reflect different natures and frequencies of items. This increases the applicability of FPM to real-life situations.
  • 28.
    More Efficient Algorithmfor Mining Frequent Patterns 5 – Based on the proposed Set-Enumeration-tree with Multiple minimum supports (ME-tree), a new sorted downward closure (SDC) property of FPs in ME-tree holds. Therefore, the baseline algorithm FP-ME can directly discover FPs by spanning the ME-tree with the SDC property, without the candidate generate- and-test, thus greatly reduce the running time and memory consumption. – The DiffSet concept is further extend in the improved FP-MEDiffSet algorithm to greatly speed up the process of mining FPs. – Extensive experiments show that the proposed FP-ME algorithm is more effi- cient than the state-of-the-art CFP-growth++ algorithm for mining FPs in terms of runtime, effectiveness of prune strategies and scalability, especially the improved algorithm considerably outperforms the baseline algorithm. 2 Related Work Up to now, many approaches have been extensively developed to mine FPs. Since only a single minsup value is used for the whole database, the model of ARM implicitly assumes that all the items in the database have similar occurrence frequencies. However, as most of the real-world databases are non-uniform in nature, mining FPs with a single minsup (or mincof ) constraint leads to the following problems: (i) If minsup is set too high, we will not find the patterns involving rare items. (ii) In order to find the patterns that involve both fre- quent and rare items, we have to set minsup very low. However, this may cause combinatorial explosion, producing many meaningless patterns. The problem of frequent pattern mining with multiple minimum support thresholds has been extensively studied, and algorithms such as MSApriori [7], CFP-growth [11], CFP-growth++ [10], REMMAR [8] and FQSP-MMS [6] have been proposed, among others [9]. MSApriori extends the well-known Apriori algorithm to mine FPs or ARs by considering multiple minimum support thresh- olds [7]. The major idea of MSApriori is that by assigning specific minimum item support (MIS) values to each item, rare ARs can be discovered without gener- ating a large number of meaningless rules. MSApriori mines ARs in a level-wise manner but suffers from the problem of “pattern explosion” since it relies on a generate-and-test approach. Lee et al. [9] then proposed a fuzzy mining algorithm for discovering useful fuzzy association rules with multiple minimum supports by using maximum constraints. An improved tree-based algorithm named CFP- growth [11] was then proposed to directly mine frequent itemsets with multiple thresholds using the pattern growth method based on a new MIS-Tree struc- ture. An enhanced version of CFP-growth named CFP-growth++ [10] was also proposed, it employs LMS (least minimum support) instead of MIN to reduce the search space and improve performance. LMS is the least MIS value amongst all MIS values of frequent items. Moreover, three improved strategies were also presented to reduce the search space and runtime. However, it is still too time- consuming and memory cost.
  • 29.
    6 W. Ganet al. Table 1. An example database TID Transaction T1 a, c, d T2 a, d, e T3 b, c T4 a, c, e T5 a, b, c, d, e T6 b, d T7 a, b, c, e T8 b, c, d T9 c, d, e T10 a, c, d Table 2. Derived FPs Itemset MIS × |D| sup Itemset MIS × |D| sup (a) 4 6 (ae) 4 4 (b) 5 5 (bc) 3 4 (c) 3 8 (cd) 3 5 (d) 6 7 (ce) 3 4 (ac) 3 5 (acd) 3 3 (ad) 4 4 (ace) 3 3 3 Preliminaries and Problem Statement Let I = {i1, i2, . . . , im} be a finite set of m distinct items. A transactional database D = {T1, T2, . . . , Tn}, where each transaction Tq ∈ D is a subset of I, contains several items with their purchase quantities q(ij, Tq), and has an unique identifier, TID. A corresponding multiple minimum supports table, MMS-table = {ms1, ms2, . . . , msm}, indicates the user-specified minimum sup- port value msj of each item ij. A set of k distinct items X = {i1, i2, . . . , ik} such that X ⊆ I is said to be a k-itemset. An itemset X is said to be contained in a transaction Tq if X ⊆ Tq. An example database is shown in Table 1. It consists of 10 transactions and 5 items, denoted from (a) to (e), respectively. For example, transaction T1 contains items a, c and d. The minimum support value of each item, denoted as ms, is defined and as shows the MMS-table = {ms(a): 40 %; ms(b): 50 %; ms(c): 30 %; ms(d): 60 %; ms(e): 100 %}. Definition 1. The number of transactions that contains an itemset is known as the occurrence frequency of that itemset. This is also called the support count of the itemset. The support of an itemset X, denoted by sup(X), is the number of transactions containing X w.r.t. X ⊆ Tq. Definition 2. The minimum support threshold of an item ij in a database D, which is related to minsup, is redenoted as ms(ij) in this paper. A structure called MMS-table indicates the minimum support threshold of each item in D and is defined as: MMS-table = {ms1, ms2, . . ., msm}. Definition 3. The minimum item support value of a k-itemset X = {i1, i2, . . . , ik} in D is denoted as MIS(X), and defined as the smallest ms value for items in X, that is: MIS(X) = min{ms(ij)|ij ∈ X}. For example, MIS(a) = min{ms(a)} = 40 %, MIS(ae) = min{ms(a), ms(e)} = min{40 %, 100 %} = 40 %, and MIS(ace) = min{ms(a), ms(c), ms(e)} = min{40 %, 30 %, 100 %} = 30 %. The extended model enables the
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    More Efficient Algorithmfor Mining Frequent Patterns 7 user to simultaneously specify high minsup for a pattern containing only fre- quent items and low minsup for a pattern containing rare items. Thus, efficiently addressing the “rare item problem”. Definition 4. An itemset X in a database D is called a frequent pattern (FP) iff its support count is no less than the minimum itemset support value of X, such that sup(X) ≥ MIS(X) × |D|. Definition 5. Let be a transactional database D (|D| = n) and a MMS-table, which defines the minimum support thresholds {ms1, ms2, . . . , msm} of each item in D. The problem of mining FPs from D with multiple minimum supports (FP-MMS) is to find all itemsets X having a support no less than MIS(X)×|D|. For the running example, the derived complete set of FPs is shown in Table 2. 4 Proposed FP-ME Algorithm for FP-MMS 4.1 Proposed ME-Tree Based on the previous studies, the search space of mining frequent patterns with multiple minimum supports can be represented as a lattice structure [12] or a Set-enumeration tree [12], both of them for the running example are respectively shown in Fig. 1. Note that the well-known downward closure property in associ- ation rule mining does not hold for FP-MMS. For example, the item (e) is not a FP but its supersets (ae) and (ace) are FPs in the running example. To solve this problem, Liu et al. [7] proposed a concept called sorted closure property, which assumes that all items within an itemset are sorted in increasing order of their minimum supports. Property 1. If a sorted k-itemset {i1, i2,. . . , ik}, for k ≥ 2 and MIS(i1) ≤ MIS(i2) ≤ . . . ≤ MIS(ik), is frequent, then all of its sorted subsets with k − 1 items are frequent, except for the subset {i2, i3, . . . , ik} [6]. Definition 6 (Total order ≺ on items). Without loss of generality, assume that items in each transaction of a database are sorted according to the lexi- cographic order. Furthermore, assume that the total order ≺ on items in the designed ME-tree is the ascending order of items MIS values. Definition 7 (Set-enumeration-tree with multiple minimum sup- ports). A ME-tree is a sorted Set-enumeration tree using the defined total order ≺ on items. Definition 8 (Extension nodes in the ME-tree). In the designed ME-tree with the total order ≺, all child nodes of any tree node are called its extension nodes. Since MIS(c) MIS(a) MIS(b) MIS(d) MIS(e), the total order ≺ on items in the ME-tree is c ≺ a ≺ b ≺ d ≺ e. The ME-tree for the running example is illustrated in Fig. 2, and the following lemmas are obtained.
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    8 W. Ganet al. e c d { } b a abc abd abce abce abde abe 3 level 5 level ab ac ad ae ade bcd bce acde bcde abcde bc bd be cd ce de cde bde acd ace 1 level 2 level 4 level e c d { } b a abc abd abcd abce abde abe 3 level 4 level ab ac ad ae ade bcd bce acde bcde abcde bc bd be cd ce de cda bde acd ace 1 level 2 level 5 level (a) A subset of lattice structure (b) A set enumeration tree Fig. 1. The search space presentation of FP-MMS. e b d { } a c cab cad cabd cabe cade cae ca cb cd ce cde abd abe cbde abde cabde ab ad ae bd be de bde ade cbe cde skipped nodes visited and pruned nodes visited nodes MIS(c) MIS(a) MIS(b) MIS(d) MIS(e) e b d { } a c cad cabd cabe cade cae ca cb cd ce cde abd abe cbde abde cabde ab ad ae bd be de bde ade cbd cbe cab (a) (b) Fig. 2. Applied pruning strategies in the ME-tree. Lemma 1. The complete search space of the proposed FP-MMS algorithm can be represented by a ME-tree where items are sorted according to the ascending order of the MIS values on items. Lemma 2. The support of a node in the ME-tree is no less than the support of any of its child nodes (extension nodes). Proof. Let Xk be a node in the ME-tree containing X items, and let Xk−1 be any parent node of Xk containing (k-1) items. It is straightforward from the well-known Apriori property that sup(Xk ) ≤ sup(Xk−1 ), this lemma can be proven. Lemma 3. The MIS of a node in the ME-tree equals to the MIS of any of its child nodes (extension nodes).
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    More Efficient Algorithmfor Mining Frequent Patterns 9 4.2 Proposed Pruning Strategies It is important to note that the sorted downward closure (SDC) property of FPs in the ME-tree can only guarantee partial anti-monotonicity for FPs, but not general anti-monotonicity. In other words, the SDC property holds for any extensions (child nodes) of a given node, but it may not hold for any supersets of that node. Thus, if the SDC property of FPs is used to determine if all supersets of an itemset should be explored, some FPs may not be found. For instance, in the running example database, the item (e) is not a FP since sup(e) = 5 (10), while its supersets (ae), (ce) and (ace) are FPs, as shown in Table 2. It is thus incorrect to directly determine the FPs based only on the proposed SDC property. In MSApriori and CFP-growth++, it was shown that the MIN/LMS concept can guarantee the global anti-monotonicity of frequent patterns with multiple minimum supports and ensure the completeness of the set of derived FPs. To address this problem, we further adopt the MIN/LMS concept in the proposed FP-ME algorithm. Definition 9 (Least minimum support, LMS). The least minimum support (LMS) refers to the lowest minsup of all frequent patterns. Therefore, the LMS in a database is always equal to the lowest MIS value among all frequent items. Thus, the LMS is equal to the lowest value in the MMS-table and is defined as min{ms(i1), ms(i2), . . . , ms(im)}, where m is the total number of items in a database. For example, the LMS of Table 2 is calculated as LMS = min{ms(a), ms(b), ms(c), ms(d), ms(e)} = min{40 %, 50 %, 30 %, 60 %, 100 %} = 30 %. Property 2. If X = {i1, i2,. . . , ik} ⊆ I, where 1 ≤ k ≤ n, is a pattern such that sup(X) LMS, then sup(X) min{MIS(i1), MIS(i2), . . . , MIS(ik)}, it never could be a frequent pattern. Property 3. If X and Y are two patterns such that X ⊂ Y and sup(X) LMS, then sup(Y ) LMS. It indicates that LMS guarantees the global anti- monotonicity of frequent patterns with multiple minimum supports. Proof. Let be an itemset X such that X is a subset of Y . Thus, sup(Y ) ≤ sup(X). The relationship sup(Y ) ≤ sup(X) LMS holds. Note that the set of 1-items which having sup(X) ≥ LMS is denoted as LMS-FP1 , the following theorem can be obtained. Theorem 1. Assume that 1-itemsets which having a MIS lower than LMS are discarded and that the sorted downward closure (SDC) property is applied. We have that if an itemset is not a LMS-FP1 , then it is not a FP as well as all its supersets. Proof. Let Xk−1 be a (k-1)-itemset and its superset k-itemset is denoted as Xk . Since Xk−1 ⊆ Xk , (1) For a LMS-FP1 , sup(X) ≥ LMS; (2) Since items are sorted by ascending order of MIS values, sup(Xk−1 ) ≥ sup(Xk )
  • 33.
    Another Random ScribdDocument with Unrelated Content
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    CHAPTER V THE COMINGOF CHRISTIANITY Some there were who had heard of Christ in the old days, but a band of monks landing on the coast of Kent brought the news again to this country. Pope Gregory had sent Augustine from Rome to tell the Saxons about Christ, for he was sorry that they loved Odin and Thor, and did not know any other god. Ethelbert, the King of Kent, had a Christian wife, and he was very anxious to know what these strangers had to say about the new God. But he was afraid that they might know how to work charms and to call out wicked spirits, so he let Augustine and his monks preach to the people out of doors, for he thought that they could not harm any one in the open air. When the Roman monks preached, many people became Christians, but the old Saxon poets sang sorrowful stories of Odin's anger, and how the gods had left the world for ever because the people were not faithful. Bede tells a story of how the old wise men of Northumbria met together to decide whether they would give up the old gods for Christ or not, and as they sat in solemn silence, thinking of this great thing, an old man rose and said, The present life of man, O King, seems to me like the swift flight of a sparrow, who on a wintry night darts into the hall, as we sit at supper. He flies from the storms of wind and rain outside, and for a brief space abides in the warmth and light, and then vanishes again into the darkness whence he came. So is the life of man, for we know not whence we came nor whither we go. Therefore if this stranger can tell us anything more certain, we should hearken gladly to him. Thus, they became Christians. They built churches in their villages; first of wood, then of stone.
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    Many Christian teachersthen came to England and built homes or monasteries, wherever they went, first of rough timber, then of stone. They made clearings in the forests and drained the fenlands, and the people followed and built houses for themselves near the monasteries, for they found that they could learn many things from the monks. The sick, the poor, the tired and the old were always welcome, and travellers too were glad to rest there, for there were no inns in those days. The monks were ruled by an abbot, and the nuns, who lived in other houses, by an abbess. They took a vow of poverty and thought that they served God best by giving their time to prayer and praise. They loved their monastery, and, as the centuries went by, they made it more and more beautiful. The people gave rich offerings and builders came from foreign lands, skilled in stonework and other arts. Carvings were made for the church, pictures were painted on the walls, and flowers and trees were brought from the Holy Land to plant in the gardens. In this way came the cedar trees and the juniper, and certain plants that now grow wild in parts of the country like the poisonous hellebore, the grape hyacinth and the little fritillary or snake's head. Great men brought gifts of frankincense and myrrh, to be burned in the church on holy days, or jewels for the altar, and silk from the east for hangings, but the greatest treasure of all was the relic. People would travel many miles to see this, for those who saw it could be healed of their sickness or forgiven for their sins. There were many curious relics. There were little bits of wood, that men believed belonged to the real cross, on which Christ was crucified, and thorns, which were said to have come from His crown. S. Louis, King of France, built the beautiful Sainte Chapelle in Paris, where he might keep the crown of thorns, which the Crusaders brought from Palestine. The monastery was usually built round a square garden or lawn. On one side was the church, on another the hall and large kitchens and pantries, for there were often visitors, some of high estate, and they must be royally feasted. In the Rule of S. Benedict it was written, Let
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    all guests whocome to the monastery be entertained like Christ Himself; because He will say, 'I was a stranger and ye took me in'. The guest-house must stand apart so that the guests, who are never wanting in a monastery, may not disquiet the brethren by their untimely arrivals. Anyone could claim a lodging for two nights, and in a few monasteries there was stabling provided for as many as three hundred horses. There was a long dormitory where the monks slept. It was the custom for them to get up at midnight to make a procession into the church by the night stairs. There they said matins and lauds (the last three psalms), and then returned to the dormitory to sleep if it were winter until daybreak, if summer till sunrise. Only those who had worked hard in the fields all day were excused. They dressed by the light of the wicks set in oil in little bowls at either end of the dormitory. In the cloisters were troughs for washing before meals, filled with water by taps; and above were little cupboards for towels. Some monasteries had a library, for they were quite rich in books. Then there was a writing room, where the scribes were busy making beautiful copies of the precious books, some skilled in writing, others in painting and illuminating. When the writing was done, the artist brought his colours to make the capital letters and the little pictures in the text. There was music to be copied too, and the accounts of the Abbey must be kept neatly. Sometimes a chronicle was made of great events that happened. It is from such books as these that we have learned much about the story of the country.
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    THE ABBEY OFCITEAUX A. Round this court, stables and barns. H. Guest houses and abbot's quarters. N. The Church. I. The kitchen. K. The dining hall. M. The dormitories. P. Cells of the scribes. R. The hospital.
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    A SERVICE INTHE CHAPEL The monks led peaceful lives in days when most men were busy about war. The monks divided the hours between sunrise and sunset into twelve equal parts, so it happened that the hour in winter was twenty minutes shorter than in summer. Every three hours, there was a service in church, prime at the first, terce at the third, sext at the sixth and none at the ninth. After prime, on summer mornings, the monks were summoned by the Abbot to the chapter house and there each man received his task. The latest business was talked about and plans were made for the coming guests. Then each monk went to his business, some to the gate to give food to the poor and help to the sick, some to work in the orchard and garden, to spin or to weave, though in some monasteries this kind of work was done for the brethren. They had their first meal at midday in the hall in silence. While they ate, one of their number, who had already had his meal, would read to them from a book of sermons or the Lives of the Saints. After grace, the Miserere (Psalm 51) was sung through the cloister. In summer, they would rest in the afternoon, in the dormitory or perhaps in the cloister, on the sunny south side, where they could read or think or pray. In winter, they worked at this time, because their nights were
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    long. Vespers wasread at sunset, then came supper. Compline ended the day, but it sometimes happened that they lingered in the warming- house to chat with one another, but this was against rules. Kings and princes found out what wise counsellors these men were and brought them to the courts to help them govern, though this was against the rules of the monastic orders. Then, in those days, Abbots began to ride forth like princes, monasteries were full of treasure and monks forsook the humbler ways of life they had once followed.
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    CHAPTER VI ALFRED ANDTHE DANES After the Saxons had been in England many years, when their weapons had grown rusty and they had almost forgotten how to fight, bands of Danes came sailing over the North Sea to plunder the land. God Almighty sent forth these fierce and cruel people like swarms of bees, says the chronicler. First, they carried away the beautiful things from the monasteries and churches, and then they came to live here. They drove the Saxons from their houses or built new villages by the side of the old ones. We know that they must have settled in Yorkshire and Lincolnshire, in Westmorland and Cumberland, because they gave Danish names to many places, such as Grimsby (Grim's town), Whitby, Appleby. In those days, the Danes grew very bold. Ships came from the west ready for war with grinning heads and carven beaks, runs the legend, the golden war banner shining in the bows. They tried to conquer the west and south, as well as the north and east. In the land of the West Saxons, many battles were fought, and still the little band of hungry, worn-out soldiers stood at bay. It was at this time that Alfred was made King and, like his father and brothers, was soon defeated and driven into Athelney, a little island in the west in the midst of a great swamp. There, he spent the winter drilling his soldiers and making plans to drive away the Danes in the spring time. A story is told of how he went into the Danish camp as a bard. He carried a harp, and while the mead cup was handed round, he sang the old sagas. When the feast was done and the chess board was brought out, the captains talked about the war, as they played their favourite game. So Alfred heard their plans.
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    The Danes weresurprised when the spring came, for Alfred drove them out of his kingdom and made them promise never to come into the land of the West Saxons again. But he did not try to drive them out of England, for he knew that it would be many years before his people would be strong enough, perhaps not until his own children were grown up. So he worked hard all his life to make his people good soldiers and thoughtful men, in order that, when the time came, they could drive the enemy across the seas and rule over the whole land in their stead. Formerly, said the King, foreigners sought wisdom and learning in this land, now we should have to get them from abroad if we would have them. Alfred found his nobles careless and idle, they loved hunting and feasting and thought very little about ruling a kingdom or leading an army. They were too old to learn, but the king made up his mind that their children should grow up good soldiers and wise rulers. So he made a school at his court for these boys. There they learned the art of war and many other things too. They read the history of their own country from Bede's Book, that had been kept at York. This book was written in Latin, so the King had to have it translated for them. He had heard of the fame of a great writer, Asser, who lived in South Wales. Messages were sent to him to ask him to come to Alfred's court to write the history of the reign. Asser did not wish to leave his beautiful home, but in the end, he promised to stay for six months every year; that is why we know so much about this great King. Alfred turned into English some beautiful old Latin books that taught men how to rule well, and in the margins he himself wrote what he thought wise counsel. Two of these books had been written by Pope Gregory who sent Augustine to England, and at the beginning of one of them there are these words, Alfred, King, turned each word of me into English and sent me to his writers, north and south, and bade
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    them make moresuch copies that he might send them to the bishops. Alfred loved reading and he wrote down all the wise sayings that he found. Asser tells the story of how the King came to do this. When we were one day sitting together in the royal chamber and were holding converse upon divers topics, as our wont was, it chanced that I repeated to him a quotation from a certain book. And when he had listened attentively to this with all his ears, and had carefully pondered it in the deep of his mind, suddenly he showed me a little book which he carried constantly in the fold of his cloak. In it were written the Daily Course and certain psalms and some prayers, which he had read in his youth, and he commanded that I should write that quotation in the same little book. And when he urged me to write that as quickly as possible, I said to him, 'Are you willing that I should write the quotation apart by itself on a small leaf? For we know not that at some time we shall not find some other such quotation or more than one, which will please you: and if it should so turn out unexpectedly we shall rejoice that we have kept this apart from the rest.' And when he heard this, he said 'Your counsel is good.' And I, hearing this and being glad, made ready a book of several leaves, in haste, and at the beginning of it I wrote that quotation according to his command. And on the same day, by his order, I wrote in the same book no less than three other quotations pleasing to him, as I had foretold. This book he used to call his handbook, because with the utmost care he kept it at his hand day and night and in it he found, as he said, no small comfort. Alfred desired to hear of other lands, but there were hardly any maps in those days and no books of geography. Great travellers were welcomed at his court, for, when he was very young, he had paid a visit to Rome and had seen a little of foreign lands. Othere, the famous seaman, who had sailed in the Arctic regions, came to tell his
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    stories of thefrozen seas that men could walk upon and of the strange midnights when the sun shone as bright as by day. Othere spoke of whales and walruses and he brought their tusks of fine ivory to show the King. Wulfstan came, too, and he had travelled in Prussia and brought stories of a land rich in honey and fish. Travellers came from the hot lands, from India and the far east. They brought presents of tiger skins and spices, of rich silks and jewels. They told stories of wonderful deserts, of the high snowy mountains and thick jungles, that they had passed on their long journey. The King delighted to read of elephants and lions and of the beast we call lynx that men said could see through trees and even stones. Or what shall I say, says the chronicler, concerning the daily intercourse with the nations which dwell from the shores of Italy unto the uttermost bounds of Ireland? for I have seen and read letters and gifts sent to Alfred by Elias, patriarch of Jerusalem. In this way the West Saxon folk heard of great, unknown countries and peoples, and the sons of the nobles learned not only to run, to ride, to swim and to make runes or rhymes, but to be great rulers and adventurers as their forefathers had been. Alfred was a very busy King, for not only had he to receive ambassadors and counsellors, but he had to ride through the land, seeing justice done, and restoring the ruined churches and monasteries. He taught the workers in gold and artificers of all kinds, to build houses majestic and good, beyond all that had been built before. What shall I say of the cities and towns which he restored, and of the others which he built, where before there had never been any? Or of the work in gold and silver, incomparably made under his directions? Or of the halls and royal chambers wonderfully made of stone and wood by his command? Or of the royal residences built of stone, moved from their former positions and most beautifully set up in more fitting places by the King's command?
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    The King gavemany gifts to the craftsmen whom he had gathered from all lands, men skilled in every earthly work, and he gave a portion to the wayfaring men who came to him from every nation, lying near and far, and who sought from him wealth, and even to those who sought it not. There were no clocks in those days and the King was much troubled, for he had promised to give up to God half his services. He could not equally distinguish the length of the hours by night, on account of the darkness: and oftentimes of the day, on account of storms and clouds. After long reflection on these things he at length, by a useful and shrewd invention, commanded his chaplain to supply wax in sufficient quantities. He caused the chaplain to make six candles of equal length, so that each candle might have twelve divisions marked upon it. These candles burned for twenty-four hours, a night and a day. But sometimes, from the violence of the wind, which blew through the doors and windows of the chambers or the canvas of the tents, they burned out before their time. The King then considered by what means he might shut out the wind; and so he ordered a lantern which was closed up, by the King's command, by a door made of horn. By this means, six candles lasted twenty-four hours, and when they went out others were lighted. Thus the King left behind him as he wished a memory in good works, and, after him, his son and daughter drove the Danes eastward beyond Watling Street. The northmen came back with the strong King Cnut, who conquered the whole country. Now Cnut was a great king before he took England, for he ruled Sweden and Denmark and was lord over Norway. When he was crowned King of England, he began to love this kingdom more than all his lands, and he made his home in London. He wanted to be a real English King, so he looked for the old laws of Alfred the Great and told the English people that he would rule as Alfred had done.
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    The King hada fine army of tall, strong soldiers, but he sent nearly all of them back to their own land and kept only three thousand house- companions for a body guard. The English people knew that he trusted them, for he could not have kept the land in order with so few soldiers, if the people had hated him. For seventeen years, there was a great peace in the land and ships could pass to and fro, carrying skins, silks, costly gems and gold, besides garments, wine, oil, ivory, with brass and copper, and tin and silver and glass and such like. When Cnut's two sons had reigned in the land, then the Saxons once more had a Saxon King.
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    CHAPTER VII THE BATTLEOF HASTINGS Edward the Confessor, the Saxon prince, had taken refuge in Normandy in the days when the Danish Kings ruled in England. There he learned to speak Norman French and to love Norman ways. When the Saxons chose him to be king, he brought some of his Norman friends to court with him. He was a man full of grace and devoted to the service of God. He left the rule of his kingdom to three Saxon Earls, Siward the Stout, a man who struck terror to the hearts of the Scots, Leofric of the Marsh land, wise in the things of God and men, and Godwin of Wessex. There was much trouble because there were no heirs to the throne, and the Norman chroniclers say that the King promised his crown to William, Duke of Normandy. The Saxons did not know this, and if they had they would not have crowned him; so they chose Harold, son of Godwin and brother of the Queen, to rule after Edward the Confessor. They chose Harold for he was a man after their own heart, strong and fearless, like the heroes of old. Harold had two elder brothers, but they were cruel and lawless and the people feared them. The Normans told a story of how Harold had been wrecked on the coast of Normandy, two years before this, and was taken before the Duke as a prisoner. The Duke would not let him go until he had sworn, with his hand upon the holy relics, that he would never claim the Saxon crown. When Edward died, Harold forgot this oath and the people crowned him with much rejoicing. When the news reached the Duke of
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    Normandy he wasin his park of Quévilly, near Rouen, with many knights and squires, going forth to the chase. He had in his hand the bow, ready strung and bent for the arrow. The messenger greeted him and took him aside to tell him. Then the Duke was very angry. Oft he tied his mantle and oft he untied it again and he spoke to no man, neither dare any man speak to him. Then he bade his men cut down the trees in the great forests and build him ships to take his soldiers to England. When they were ready, there arose a great storm and for many weeks he waited by the sea shore for a fair wind and a good tide. Tostig, too, Harold's brother, became very jealous and asked for a half of the kingdom. And because Harold would not listen, Tostig went to Norway, to beg the great King Hadrada to call out his men and ships and sail for England. So the Northmen sailed up the river Humber and took York. Then, Harold and his soldiers marched to the North to fight against Tostig. When he had pitched his camp, he sent word to Tostig, King Harold, thy brother, sends thee greeting, saying that thou shalt have the whole of Northumbria or even the third of his kingdom, if thou wilt make peace with him. But, said Tostig, what shall be given to the King of Norway for his trouble? Seven feet of English ground, was the answer, or as much more as is needful, seeing that he is taller than other men. Then said the Earl, Go now and tell King Harold to get ready for battle, for never shall the Northmen say that Tostig left Hadrada, King of Norway, to join the enemy. And when Harold departed, the King of Norway asked who it was that had spoken so well. That, said Tostig, was my brother Harold. When Hadrada heard this he said, That English king was a little man, but he stood strong in his stirrups. A great fight there was, and Hadrada fought fiercely, but he was killed by an arrow. When the sun set, the Northmen turned and fled, for Tostig, too, lay dead upon the field. That night there was a great feast in the Saxon camp. As they held wassail, a messenger came riding into the camp, breathless with haste, for he had rested not day nor night in the long ride to the North. He shouted to those who stood by, The Normans— the Normans are come—they have landed at Hastings—Thy land, O
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    King, they willwrest from thee, if thou canst not defend it well. That night, the Saxons broke up their camp and hurried towards London. The wise men begged Harold to burn the land, that the enemy might starve, but Harold would not, for he said, How can I do harm to my own people? So they rode off to meet the Duke near Hastings. Now Harold chose his battle-field very wisely, a rising ground, for most of his soldiers were on foot and many of the Normans were on horse- back and the King knew that it was hard riding up hill. So Harold stood under the Golden Dragon of Wessex watching the enemy below. In the front of the Normans rode their minstrel, throwing his sword into the air and catching it again, as he sang of the brave deeds of those knights of old, Roland and Oliver. Fierce was the onslaught, and soon the Normans turned to flee. Then were the Saxons so eager for the spoil that they came down from their high ground to chase the enemy. When the Duke saw this, he wheeled his men in battle array and the fight began again fiercer than ever. Then the Duke ordered a great shower of arrows to be shot up into the air, so that when they fell, they pierced many a good soldier. And Harold fell, shot through the eye by an arrow. Still, the Saxons fought on, for they held it shame to escape alive from the fields whereon their leader lay slain. That night, William pitched his tent where the King's banner had waved. Then came Gyda the mother of Harold to beg Harold's body from the Duke. But he gave orders that it should be buried by the seashore, Harold guarded the cliffs when he was alive, let him guard them, now that he is dead, said William. So the King's mother and his brothers hid in the rocky west, in Tintagel, for fear of the Duke's anger. Then did William march slowly to London, burning and harrying the land, and all men feared him.
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    HAROLD DEFEATS ANDKILLS TOSTIG AND THE KING OF NORWAY AT STAMFORD BRIDGE
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    A BATTLE INTHE 15TH CENTURY There is a piece of tapestry still kept at Bayeux in France, showing how England was conquered. It was probably made later than the reign of William and perhaps was intended to go round the walls of the choir of Bayeux Cathedral, for it has been measured and found to be of the right length. Though it is old and torn and faded, we have been able to learn many things from it [2]. There were few histories written in those days, for the Normans were too busy fighting for their new lands and the English were too sorrowful to tell their story.
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    [2] There isa copy in Reading Museum. See Guide to Bayeux Tapestry, published by Textile Department, Victoria and Albert Museum.
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    CHAPTER VIII THE NORMANKINGS The strong men of the north had not bowed to William the Conqueror on the field of Hastings, and when they heard that he was crowned, they armed themselves against him. The King marched towards the north slowly, burning and harrying the land as he passed, and his path was marked by flaming villages and hayricks. When he came into Yorkshire, he laid waste the land, and for nine years not an acre was tilled beyond the Humber, and dens of wild beasts and of robbers, to the great terror of the traveller, alone were to be seen. The Saxons fled; some died of hunger by the wayside, some sold themselves as slaves, and a few hid themselves in the Fens, a great stretch of water and marsh land, in the east, dotted here and there with islands and sometimes crossed in winter on sledges. There Hereward the Wake built his camp in the swamps of Ely and there all true men gathered round him. He was bold and hardy and even William said of him, if there had been in England three such men as he, they would have driven out the Normans. The King gave orders that a causeway should be built across the Fens and he besieged the Saxons in Ely, and some said that Hereward was betrayed. But William pardoned him and sent him to Normandy to command his army. Many stories are told of his adventures. It was said that he was slain one day as he slept in an orchard, for there were many in the King's court who envied him.
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    The Conqueror wasa wise king, and he desired to know what manner of kingdom he had conquered. He held a great council and very deep speech with his wise men about this land, how it was peopled and by what men. So he sent his clerks to every shire and commanded them to write down on a great roll all that they could find out about the country. They were to ask of the lord and of the freemen in the villages and of the monks in the monasteries these questions: How much land have you? Who gave you that land? What services do you owe the King for it? Have you paid them? How many people dwell upon your land? How many soldiers must you lend to the King if need be? How many cattle have you? Have you a mill? (if they had, they owed every third penny to the King). Have you a fish pond? (fish was a great luxury). The lords and the monks were unwilling to answer, for they knew they must pay to the King all that was due. So narrowly did the King make them seek out all this that there was not a single yard of land (shameful it is to tell, though he thought it no shame to do) nor one ox, nor one cow, nor one swine left out, that was not set down in his rolls, and all these rolls were afterwards brought to him. These records are called Domesday Book. The Kings, when they desired to get money or soldiers from the great lords and monks, turned to the Domesday Book. When the book was brought to the King, he summoned the lords and freemen to come to do him homage. These men came and they placed their hands between the King's hands and, kneeling before him, they promised to be the King's men and to follow him in time of need. Hear, my lord, said the baron, I become liege man of yours for life and limb … and I will keep faith and loyalty to you for life and death, God help me. William I made great peace in the land, and, as he was dying, he called his three sons to him, and to Robert, the eldest, he gave Normandy and to William Rufus, England. Then Henry turned
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    sorrowfully to hisfather, And what, my father, do you give to me? The King replied, I bequeath £500 to you from my treasury. Then said Henry, What shall I do with this money, having no corner of the earth I can call my own? But his father replied, My son, be content with your lot and trust heaven, Robert will have Normandy and William England. But you also in your turn will rule over the lands which are mine and you will be greater and richer than either of your brothers. Rufus ruled over England thirteen years, and he was hated by the people. Robert gave Normandy to his brother for a sum of money; and thus Henry, when Rufus was dead, became Duke of Normandy and King of England. He married a Saxon lady and there was great awe of him in the land, he made peace for man and beast.
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    CHAPTER IX THE NORMANBARONS The Norman barons who came to England with William the Conqueror were much disappointed, for they had hoped to share the kingdom with him and to be great lords. But William had not given them as much land as they desired, and he had made Domesday Book so that they should render to him due service and payment in return for his gifts. The barons had not always paid that which they owed; and Henry I made a rule that all should come to his Court three times a year, to Winchester at the feast of Easter, to Westminster at Whitsuntide and to Gloucester at Mid-winter, when he wore his crown, and then they should do homage and pay their taxes. To this court came the officers of the household, and the King appointed a Bishop to receive the money and priests to keep the accounts, since there were few among the nobles or citizens who could read, write and add figures. The money was counted out on a chequered table, and so the court came to be called the Exchequer. The barons could not easily cheat the King; for, when their money had been counted out upon the table, some of it was melted on the furnace, lest it should contain base metal, and it was weighed in the balances, lest the coins should have been clipped. Then Domesday Book was searched and the priests read out what sum was due to the King from this lord. When the Chancellor was satisfied, a tally was handed to the baron. This was a willow or hazel stick, shaped something like the blade of a knife, about an inch thick. Notches were cut in it to show the amount
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    paid and thehalfpennies were marked by small holes. The tally was then split down the middle through the notches, and the baron took one half so that he might show it to the Chancellor when he came to court to pay again, and the Chancellor kept the other half to prove that the baron was not cheating. Thus the King kept his barons in order and there was peace in the land. Now Henry I had an only son, and to him he gave a ship, a better one than which there did not seem to be in the fleet, but as he was sailing from Normandy to England, it struck upon a rock and all perished, save only a butcher, who was found in the morning clinging to a plank. When the King heard the news, he was in great distress; for no woman had yet ruled in England and his daughter Matilda was married to a French Count, whom all the Normans hated for his fierce temper and overbearing ways. The King, nevertheless, made them swear to put her on the throne, but, when he died, the barons chose her cousin, Stephen, for he was a mild man, soft and good, and did no justice. Stephen quarrelled with the Chancellor and closed the Court of Exchequer where the barons had paid their dues, and he let the barons build castles and coin their own money. When he was in need of soldiers, he hired foreign ruffians, and because he could not pay them, he let them loose upon the land to plunder: thus he undid all his cousins had done. The barons forswore themselves and broke their troth, for every nobleman made him a castle and held it against the King and filled the land full of castles. They put the wretched country folk to sore toil with their castle-building; and, when the castles were made, they filled them with devils and evil men. Then they took all those that they deemed had any goods, both by night and day, men and women alike, and put them in prison to get their gold and silver, and tortured them with tortures unspeakable. Many thousands they slew with hunger. I
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    cannot nor maynot tell all the horrors and all the tortures that they laid on wretched men in the land. And this lasted nineteen winters, while Stephen was King, and ever it was worse and worse. They laid taxes on the villages continually, and, when the wretched folk had no more to give them, they robbed and burned all the villages, so that thou mightest easily fare a whole day's journey and shouldst never find a man living in a village nor a land tilled. Then was corn dear, and flesh and cheese, and there was none in the land. If two or three men came riding to a village, all the village folk fled before them, deeming them to be robbers. Wheresoever men tilled, the earth bore no corn, for the land was fordone with such deeds, and they said openly that Christ and His Saints slept. Such, and more than we can say, we suffered nineteen winters for our sins. Then Stephen made a treaty with Matilda's son Henry and promised him the crown of England; for Henry was already a great prince, holding more lands than the monarch of France. Moreover, he was valiant in battle, strong in the Council chamber and never weary. The French King said of him, Henry is now in England, now in Ireland, now in Normandy, he may be rather said to fly than go by horse or boat. Henry II could ride all night and, if need were, sleep in the saddle. His legs were bruised and livid with riding. He was given beyond measure to the pleasures of hunting; and he would start off the first thing in the morning on a fleet horse and now traversing the woodland glades, now plunging into the forest itself, now crossing the ridges of the hills, would in this manner pass day after day in unwearied exertion; and when, in the evening, he reached home, he was rarely seen to sit down whether before or after supper. In spite of all the fatigue he had undergone, he would keep the whole court standing. This tireless ruler, before he became King, had restored order in England, for he commanded the hired soldiers to be gone immediately, and they went as they had come like a flight of locusts. He destroyed
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    more than athousand castles, and those that were well built he kept for himself. All folk loved him, for he did good justice. He opened the Court of Exchequer, so that the Barons were forced to pay all they owed Stephen for the nineteen years of his reign. He visited all the courts of justice in the land, and no man durst do evil, for none knew where the King might be. He appointed judges to travel round the country and to sit at Westminster and hear complaints, for many had sought the King in vain, so swiftly did he travel from place to place. Thus the barons were made to fear the King and rule justly.
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