Even as computer processing speeds have become faster and the size of memory has also increased over the years, the need for elegant algorithms (programs that accomplish such tasks/operations as information retrieval, and manipulation as efficiently as possible) remain as important now as it did in the past. It is even more so as more complex problems come to the fore. Skip List is a probabilistic data structure with algorithms to efficiently accomplish such operations as search, insert and delete. In this paper, we present the results of implementing the Skip List data structure. The paper also addresses current Web search strategies and algorithms and how the application of Skip List implementation techniques and extensions can bring about optimal search query results.
Parallel Key Value Pattern Matching Modelijsrd.com
Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.
EFFICIENT SCHEMA BASED KEYWORD SEARCH IN RELATIONAL DATABASESIJCSEIT Journal
Keyword search in relational databases allows user to search information without knowing database
schema and using structural query language (SQL). In this paper, we address the problem of generating
and evaluating candidate networks. In candidate network generation, the overhead is caused by raising the
number of joining tuples for the size of minimal candidate network. To reduce overhead, we propose
candidate network generation algorithms to generate a minimum number of joining tuples according to the
maximum number of tuple set. We first generate a set of joining tuples, candidate networks (CNs). It is
difficult to obtain an optimal query processing plan during generating a number of joins. We also develop a
dynamic CN evaluation algorithm (D_CNEval) to generate connected tuple trees (CTTs) by reducing the
size of intermediate joining results. The performance evaluation of the proposed algorithms is conducted
on IMDB and DBLP datasets and also compared with existing algorithms.
1) The document discusses a review of semantic approaches for nearest neighbor search. It describes using an ontology to add a semantic layer to an information retrieval system to relate concepts using query words.
2) A technique called spatial inverted index is proposed to locate multidimensional information and handle nearest neighbor queries by finding the hospitals closest to a given address.
3) Several semantic approaches are described including using clustering measures, specificity measures, link analysis, and relation-based page ranking to improve search and interpret hidden concepts behind keywords.
Data Mining of Project Management Data: An Analysis of Applied Research Studies.Gurdal Ertek
Data collected and generated through and posterior to projects, such as data residing in project management software and post project review documents, can be a major source of actionable insights and competitive advantage. This paper presents a rigorous
methodological analysis of the applied research published in academic literature, on the application of data mining (DM) for project management (PM). The objective of the paper is to provide a comprehensive analysis and discussion of where and how data mining is applied for project management data and to provide practical insights for future research in the field.
https://dl.acm.org/citation.cfm?id=3176714
https://ertekprojects.com/ftp/papers/2017/ertek_et_al_2017_Data_Mining_of_Project_Management_Data.pdf
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
A Trinity Construction for Web Extraction Using Efficient AlgorithmIOSR Journals
This document describes a proposed system for web extraction using a trinity construction and efficient algorithms. It begins with an abstract discussing how trinity characteristics can be used to automatically extract content from websites in a sequential tree structure. It then discusses the existing system which uses trinity tree and prefix/suffix sorting but has limitations. The proposed system introduces fuzzy logic for multi-perspective crawling across multiple websites. A genetic algorithm is used to load extracted content into the trinity structure and remove unwanted data. Finally, an ant colony optimization algorithm is used to obtain an effective structure and suggest optimized solutions.
IRJET-Efficient Data Linkage Technique using one Class Clustering Tree for Da...IRJET Journal
This document proposes a new one-to-many data linkage technique using a One-Class Clustering Tree (OCCT) to link records from different datasets. The technique constructs a decision tree where internal nodes represent attributes from the first dataset and leaves represent attributes from the second dataset that match. It uses maximum likelihood estimation for splitting criteria and pre-pruning to reduce complexity. The method is applied to the database misuse domain to identify common and malicious users by analyzing access request contexts and accessible data. Evaluation shows the technique achieves better precision and recall than existing methods.
Query Optimization Techniques in Graph Databasesijdms
Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for
storing and managing data with graph-like structure. Today, they represent a requirementfor many
applications that manage graph-like data,like social networks.Most of the techniques, applied to optimize
queries in graph databases, have been used in traditional databases, distribution systems,… or they are
inspired from graph theory. However, their reuse in graph databases should take care of the main
characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to
efficiently access data relationships. In this paper, we survey the query optimization techniques in graph
databases. In particular,we focus on the features they have in
Parallel Key Value Pattern Matching Modelijsrd.com
Mining frequent itemsets from the huge transactional database is an important task in data mining. To find frequent itemsets in databases involves big decision in data mining for the purpose of extracting association rules. Association rule mining is used to find relationships among large datasets. Many algorithms were developed to find those frequent itemsets. This work presents a summarization and new model of parallel key value pattern matching model which shards a large-scale mining task into independent, parallel tasks. It produces a frequent pattern showing their capabilities and efficiency in terms of time consumption. It also avoids the high computational cost. It discovers the frequent item set from the database.
EFFICIENT SCHEMA BASED KEYWORD SEARCH IN RELATIONAL DATABASESIJCSEIT Journal
Keyword search in relational databases allows user to search information without knowing database
schema and using structural query language (SQL). In this paper, we address the problem of generating
and evaluating candidate networks. In candidate network generation, the overhead is caused by raising the
number of joining tuples for the size of minimal candidate network. To reduce overhead, we propose
candidate network generation algorithms to generate a minimum number of joining tuples according to the
maximum number of tuple set. We first generate a set of joining tuples, candidate networks (CNs). It is
difficult to obtain an optimal query processing plan during generating a number of joins. We also develop a
dynamic CN evaluation algorithm (D_CNEval) to generate connected tuple trees (CTTs) by reducing the
size of intermediate joining results. The performance evaluation of the proposed algorithms is conducted
on IMDB and DBLP datasets and also compared with existing algorithms.
1) The document discusses a review of semantic approaches for nearest neighbor search. It describes using an ontology to add a semantic layer to an information retrieval system to relate concepts using query words.
2) A technique called spatial inverted index is proposed to locate multidimensional information and handle nearest neighbor queries by finding the hospitals closest to a given address.
3) Several semantic approaches are described including using clustering measures, specificity measures, link analysis, and relation-based page ranking to improve search and interpret hidden concepts behind keywords.
Data Mining of Project Management Data: An Analysis of Applied Research Studies.Gurdal Ertek
Data collected and generated through and posterior to projects, such as data residing in project management software and post project review documents, can be a major source of actionable insights and competitive advantage. This paper presents a rigorous
methodological analysis of the applied research published in academic literature, on the application of data mining (DM) for project management (PM). The objective of the paper is to provide a comprehensive analysis and discussion of where and how data mining is applied for project management data and to provide practical insights for future research in the field.
https://dl.acm.org/citation.cfm?id=3176714
https://ertekprojects.com/ftp/papers/2017/ertek_et_al_2017_Data_Mining_of_Project_Management_Data.pdf
Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. This does not automatically guarantee that it will be highly efficient since one may still encounter the combinatorial problem of candidate generation if one simply uses this FP-tree to generate and check all the candidate patterns. we study how to explore the compact information stored in an FP-tree, develop the principles of frequent-pattern growth by examination of our running example, explore how to perform further optimization when there exit a single prefix path in an FP-tree, and propose a frequent- pattern growth algorithm, FP-growth, for mining the complete set of frequent patterns using FP-tree.
A Trinity Construction for Web Extraction Using Efficient AlgorithmIOSR Journals
This document describes a proposed system for web extraction using a trinity construction and efficient algorithms. It begins with an abstract discussing how trinity characteristics can be used to automatically extract content from websites in a sequential tree structure. It then discusses the existing system which uses trinity tree and prefix/suffix sorting but has limitations. The proposed system introduces fuzzy logic for multi-perspective crawling across multiple websites. A genetic algorithm is used to load extracted content into the trinity structure and remove unwanted data. Finally, an ant colony optimization algorithm is used to obtain an effective structure and suggest optimized solutions.
IRJET-Efficient Data Linkage Technique using one Class Clustering Tree for Da...IRJET Journal
This document proposes a new one-to-many data linkage technique using a One-Class Clustering Tree (OCCT) to link records from different datasets. The technique constructs a decision tree where internal nodes represent attributes from the first dataset and leaves represent attributes from the second dataset that match. It uses maximum likelihood estimation for splitting criteria and pre-pruning to reduce complexity. The method is applied to the database misuse domain to identify common and malicious users by analyzing access request contexts and accessible data. Evaluation shows the technique achieves better precision and recall than existing methods.
Query Optimization Techniques in Graph Databasesijdms
Graph databases (GDB) have recently been arisen to overcome the limits of traditional databases for
storing and managing data with graph-like structure. Today, they represent a requirementfor many
applications that manage graph-like data,like social networks.Most of the techniques, applied to optimize
queries in graph databases, have been used in traditional databases, distribution systems,… or they are
inspired from graph theory. However, their reuse in graph databases should take care of the main
characteristics of graph databases, such as dynamic structure, highly interconnected data, and ability to
efficiently access data relationships. In this paper, we survey the query optimization techniques in graph
databases. In particular,we focus on the features they have in
SPATIAL R-TREE INDEX BASED ON GRID DIVISION FOR QUERY PROCESSINGijdms
Tracing moving objects have turned out to be essential in our life and have a lot of uses like: GPS guide,
traffic monitor based administrations and location-based services. Tracking the changing places of objects
has turned into important issues. The moving entities send their positions to the server through a system
and large amount of data is generated from these objects with high frequent updates so we need an index
structure to retrieve information as fast as possible. The index structure should be adaptive, dynamic to
monitor the locations of objects and quick to give responses to the inquiries efficiently. The most wellknown
kinds of queries strategies in moving objects databases are Rang, Point and K-Nearest Neighbour
and inquiries. This study uses R-tree method to get detailed range query results efficiently. But using R-tree
only will generate much overlapping and coverage between MBR. So R-tree by combining with Gridpartition
index is used because grid-index can reduce the overlap and coverage between MBR. The query
performance will be efficient by using these methods. We perform an extensive experimental study to
compare the two approaches on modern hardware.
Data characterization towards modeling frequent pattern mining algorithmscsandit
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle
extremely huge amount of data, it is quite natural that the demand for the computational
environment to accelerates, and scales out big data applications increases. The important thing
is, however, the behavior of big data applications is not clearly defined yet. Among big data
applications, this paper specifically focuses on stream mining applications. The behavior of
stream mining applications varies according to the characteristics of the input data. The
parameters for data characterization are, however, not clearly defined yet, and there is no study
investigating explicit relationships between the input data, and stream mining applications,
either. Therefore, this paper picks up frequent pattern mining as one of the representative
stream mining applications, and interprets the relationships between the characteristics of the
input data, and behaviors of signature algorithms for frequent pattern mining.
Abstract—Since the demand for information retrieval increases quickly, indexing structures became an important issue to support fast information retrieval. According to the work in this paper, a new data structure called Dynamic Ordered Multi-field Index (DOMI) for information retrieval has been introduced. It is based on radix trees organized in segments in addition to a hash table to point to the roots of each segment, where each segment is dedicated to store the values of a single field. The hash table is used to access the needed segments directly without traversing the upper segments. So, DOMI improves look-up performance for queries addressing to a single field. In the case of multiple queries addressing, each segment of the radix tree is traversed sequentially without visiting the unrelated branches. The use of segmentation for the proposed DOMI provides flexibility for minimizing communication overhead in the distributed system. Every field in the radix tree is represented by one segment, where each segment can be stored as one block.
In addition to, the proposed DOMI consumes less space comparing to indexes which are built using B or B+ trees. Hence, it is more suitable for intensive-data such as Big Data.
PAS: A Sampling Based Similarity Identification Algorithm for compression of ...rahulmonikasharma
Generally, Users perform searches to satisfy their information needs. Now a day’s lots of people are using search engine to satisfy information need. Server search is one of the techniques of searching the information. the Growth of data brings new changes in Server. The data usually proposed in timely fashion in server. If there is increase in latency then it may cause a massive loss to the enterprises. The similarity detection plays very important role in data. while there are many algorithms are used for similarity detection such as Shingle, Simhas TSA and Position Aware sampling algorithm. The Shingle Simhash and Traits read entire files to calculate similar values. It requires the long delay in growth of data set value. instead of reading entire Files PAS sample some data in the form of Unicode to calculate similarity characteristic value.PAS is the advance technique of TSA. However slight modification of file will trigger the position of file content .Therefore the failure of similarity identification is there due to some modifications.. This paper proposes an Enhanced Position-Aware Sampling algorithm (EPAS) to identify file similarity for the Server. EPAS concurrently samples data blocks from the modulated file to avoid the position shift by the modifications. While there is an metric is proposed to measure the similarity between different files and make the possible detection probability close to the actual probability. In this paper describes a PAS algorithm to reduce the time overhead of similarity detection. Using PAS algorithm we can reduce the complication and time for identifying the similarity. Our result demonstrate that the EPAS significantly outperforms the existing well known algorithms in terms of time. Therefore, it is an effective approach of similarity identification for the Server.
The document proposes an improved clustering algorithm for social network analysis. It combines BSP (Business System Planning) clustering with Principal Component Analysis (PCA) to group social network objects into classes based on their links and attributes. Specifically, it applies PCA before BSP clustering to reduce the dimensionality of the social network data and retain only the most important variables for clustering. This improves the BSP clustering results by focusing on the key information in the social network.
Detecting Gender-bias from Energy Modeling Jobscapeyungahhh
Despite of many strides made by women in the workplace, workplace inequality persists, whether they come from geographical cultures or a result of workplace attrition. Unconscious (or implicit) bias can creep
into job listings and can actively or passively discourage certain applicants’ pool. The goal of this study is to understand the role of gender bias in the energy modeling job market.
Job listings and their word choices can significantly affect the application pool. This study investigates the word choices from job postings specifically for the energy modeling sector within the United States building construction industry, using web-scraped data of an online job site. The study explores the potential usage of gender-bias terminology through frequency analysis and Natural Language Processing Kit. The study also employs two machine learning methods (one supervise and one unsupervised) to test the application in this
context and evaluate its utilization.
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering techniques to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The document reviews different clustering algorithms and argues that clustering can help organize retrieval results and improve the user experience of finding relevant information.
IRJET- Customer Online Buying Prediction using Frequent Item Set MiningIRJET Journal
The document discusses using machine learning techniques like frequent itemset mining to analyze customer purchase data and predict online buying behavior. It proposes a parallel frequent itemset mining algorithm to efficiently process large datasets. The algorithm divides the data into parts, mines frequent itemsets from each part in parallel, and then combines the results. This approach improves storage capacity, computation, and performance compared to traditional frequent itemset mining algorithms. The goal is to provide useful insights into customer purchasing decisions to help businesses.
On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Vision Based Deep Web data Extraction on Nested Query Result RecordsIJMER
This document summarizes a research paper on vision-based deep web data extraction from nested query result records. It proposes a technique to extract data from web pages using different font styles, sizes, and cascading style sheets. The extracted data is then aligned into a table using alignment algorithms, including pair-wise, holistic, and nested-structure alignment. The goal is to remove immaterial information from query result pages to facilitate analysis of the extracted data.
1) This document discusses different techniques for cross-domain data fusion, including stage-based, feature-level, probabilistic, and multi-view learning methods.
2) It reviews literature on data fusion definitions, implementations, and techniques for handling data conflicts. Common steps in data fusion are data transformation, schema mapping, and duplicate detection.
3) The proposed system architecture performs data cleaning, then applies stage-based, feature-level, probabilistic, and multi-view learning fusion methods before analyzing dataset, hardware, and software requirements.
In the recent years the scope of data mining has evolved into an active area of research because of the previously unknown and interesting knowledge from very large database collection. The data mining is applied on a variety of applications in multiple domains like in business, IT and many more sectors. In Data Mining the major problem which receives great attention by the community is the classification of the data. The classification of data should be such that it could be they can be easily verified and should be easily interpreted by the humans. In this paper we would be studying various data mining techniques so that we can find few combinations for enhancing the hybrid technique which would be having multiple techniques involved so enhance the usability of the application. We would be studying CHARM Algorithm, CM-SPAM Algorithm, Apriori Algorithm, MOPNAR Algorithm and the Top K Rules.
A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining iosrjce
Internet is the most active and happening part of everyone’s life today. Almost every business or
service or organization has its website and performance of the site is an important issue. Web usage mining
based on web logs is an important methodology for optimizing website’s performance over the internet.
Different mining techniques like Apriori method, FP Tree methodology, K-Means method etc. have been
proposed by different researchers in order to make the data mining more effective and efficient. Many people
have modeled Apriori or FP Tree in their own way to increase data mining productiveness. Wu proposed
Apriori Growth as a hybrid of Apriori and FP Tree algorithm and improved FP Tree by mining using Apriori
and removed the complexity involved in FP Growth mining. Lee proposed FP Split Tree as a variant of FP Tree
and reduced the complexity by scanning the database only once against twice in FP Tree method. This research
proposes a new hybrid algorithm of FP Split and Apriori growth which combines the positives of both the
algorithms to create a new technique which provides with a better performance over the traditional methods.
The new proposed algorithm was implemented in java language on web logs obtained from IIS server and the
computational results of the proposed method performs better than traditional FP Tree method, Apriori
Method.
Dissertation data analysis in management science tutors india.com for my man...Tutors India
Management Science is very much crucial in management decision making. The primary purpose of decision-making is for effective and efficient utilization of scarce or the limited resources for which there are both private and public sectors of that economy. The present article helps the USA, the UK, Europe and the Australian students pursuing their master’s degree to identify the best data analysis, which is usually considered to be challenging. Tutors India offers UK dissertation in various Domains.
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This document summarizes an article that proposes an improved association rule mining method using correlation techniques. It integrates positive and negative rule mining concepts with frequent pattern mining to generate unique rules for classification without ambiguity. The method first constructs an FP-tree to efficiently find frequent itemsets. It then uses correlations between itemsets and class labels to determine whether rules belong to the positive or negative rule set. Classification of new data is performed by applying the matching rule subsets. The proposed method aims to address issues with traditional association rule and frequent pattern mining approaches for large datasets.
This document presents a study on finding profitable and preferable itemsets from transactional datasets. It proposes algorithms to find the top-k profitable and popular product itemsets within given utility and support threshold constraints. The paper describes the system structure, which includes modules for preprocessing product and user datasets, identifying frequent itemsets, finding smaller frequent sets, price correlation analysis, and determining the top-k popular products. An example is provided to illustrate the frequent itemset mining process. An experimental comparative study on real and synthetic datasets demonstrates the effectiveness and efficiency of the proposed algorithms.
The Best Start Project is a 7-week program that provides tools and resources to help new pastors and congregations have a successful start in their ministry together. It aims to build healthy relationships, ease grief over past leadership, gain community insights, and align on a shared vision for the church's future. The program includes sermon resources, facilitated discussions, strategic planning, and quarterly coaching. It is led by Ken Crawford and Whit Dreher and costs between $4,750-$9,500 based on congregation size.
This document discusses various methods for slope stabilization, including avoiding unstable areas, preventing instability through proper cut and fill slopes, and stabilizing existing slides. It covers the use of vegetation and drainage for prevention and stabilization, as well as structural approaches like gabions, retaining walls, reinforced fills, soil nails and anchors when stabilization is needed. The key messages are to use commonly stable slope angles, understand why failures occur, select the least costly effective stabilization measure, and ensure structures are founded on stable ground.
The document is a student paper on slope stability analysis. It was prepared by Riyaz Ahmad Bhat, a civil engineering student at the Department of civil engineering and technology. The paper discusses slope stability analysis, including the objectives of analysis, conventional methods like limit equilibrium, and numerical methods. It also describes visiting mountain slopes in Kashmir to study the effects of tectonic activity and an earthquake in 2005. The conclusion is that the paper helps understand basic concepts and procedures of slope stability analysis.
SPATIAL R-TREE INDEX BASED ON GRID DIVISION FOR QUERY PROCESSINGijdms
Tracing moving objects have turned out to be essential in our life and have a lot of uses like: GPS guide,
traffic monitor based administrations and location-based services. Tracking the changing places of objects
has turned into important issues. The moving entities send their positions to the server through a system
and large amount of data is generated from these objects with high frequent updates so we need an index
structure to retrieve information as fast as possible. The index structure should be adaptive, dynamic to
monitor the locations of objects and quick to give responses to the inquiries efficiently. The most wellknown
kinds of queries strategies in moving objects databases are Rang, Point and K-Nearest Neighbour
and inquiries. This study uses R-tree method to get detailed range query results efficiently. But using R-tree
only will generate much overlapping and coverage between MBR. So R-tree by combining with Gridpartition
index is used because grid-index can reduce the overlap and coverage between MBR. The query
performance will be efficient by using these methods. We perform an extensive experimental study to
compare the two approaches on modern hardware.
Data characterization towards modeling frequent pattern mining algorithmscsandit
Big data quickly comes under the spotlight in recent years. As big data is supposed to handle
extremely huge amount of data, it is quite natural that the demand for the computational
environment to accelerates, and scales out big data applications increases. The important thing
is, however, the behavior of big data applications is not clearly defined yet. Among big data
applications, this paper specifically focuses on stream mining applications. The behavior of
stream mining applications varies according to the characteristics of the input data. The
parameters for data characterization are, however, not clearly defined yet, and there is no study
investigating explicit relationships between the input data, and stream mining applications,
either. Therefore, this paper picks up frequent pattern mining as one of the representative
stream mining applications, and interprets the relationships between the characteristics of the
input data, and behaviors of signature algorithms for frequent pattern mining.
Abstract—Since the demand for information retrieval increases quickly, indexing structures became an important issue to support fast information retrieval. According to the work in this paper, a new data structure called Dynamic Ordered Multi-field Index (DOMI) for information retrieval has been introduced. It is based on radix trees organized in segments in addition to a hash table to point to the roots of each segment, where each segment is dedicated to store the values of a single field. The hash table is used to access the needed segments directly without traversing the upper segments. So, DOMI improves look-up performance for queries addressing to a single field. In the case of multiple queries addressing, each segment of the radix tree is traversed sequentially without visiting the unrelated branches. The use of segmentation for the proposed DOMI provides flexibility for minimizing communication overhead in the distributed system. Every field in the radix tree is represented by one segment, where each segment can be stored as one block.
In addition to, the proposed DOMI consumes less space comparing to indexes which are built using B or B+ trees. Hence, it is more suitable for intensive-data such as Big Data.
PAS: A Sampling Based Similarity Identification Algorithm for compression of ...rahulmonikasharma
Generally, Users perform searches to satisfy their information needs. Now a day’s lots of people are using search engine to satisfy information need. Server search is one of the techniques of searching the information. the Growth of data brings new changes in Server. The data usually proposed in timely fashion in server. If there is increase in latency then it may cause a massive loss to the enterprises. The similarity detection plays very important role in data. while there are many algorithms are used for similarity detection such as Shingle, Simhas TSA and Position Aware sampling algorithm. The Shingle Simhash and Traits read entire files to calculate similar values. It requires the long delay in growth of data set value. instead of reading entire Files PAS sample some data in the form of Unicode to calculate similarity characteristic value.PAS is the advance technique of TSA. However slight modification of file will trigger the position of file content .Therefore the failure of similarity identification is there due to some modifications.. This paper proposes an Enhanced Position-Aware Sampling algorithm (EPAS) to identify file similarity for the Server. EPAS concurrently samples data blocks from the modulated file to avoid the position shift by the modifications. While there is an metric is proposed to measure the similarity between different files and make the possible detection probability close to the actual probability. In this paper describes a PAS algorithm to reduce the time overhead of similarity detection. Using PAS algorithm we can reduce the complication and time for identifying the similarity. Our result demonstrate that the EPAS significantly outperforms the existing well known algorithms in terms of time. Therefore, it is an effective approach of similarity identification for the Server.
The document proposes an improved clustering algorithm for social network analysis. It combines BSP (Business System Planning) clustering with Principal Component Analysis (PCA) to group social network objects into classes based on their links and attributes. Specifically, it applies PCA before BSP clustering to reduce the dimensionality of the social network data and retain only the most important variables for clustering. This improves the BSP clustering results by focusing on the key information in the social network.
Detecting Gender-bias from Energy Modeling Jobscapeyungahhh
Despite of many strides made by women in the workplace, workplace inequality persists, whether they come from geographical cultures or a result of workplace attrition. Unconscious (or implicit) bias can creep
into job listings and can actively or passively discourage certain applicants’ pool. The goal of this study is to understand the role of gender bias in the energy modeling job market.
Job listings and their word choices can significantly affect the application pool. This study investigates the word choices from job postings specifically for the energy modeling sector within the United States building construction industry, using web-scraped data of an online job site. The study explores the potential usage of gender-bias terminology through frequency analysis and Natural Language Processing Kit. The study also employs two machine learning methods (one supervise and one unsupervised) to test the application in this
context and evaluate its utilization.
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering techniques to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The document reviews different clustering algorithms and argues that clustering can help organize retrieval results and improve the user experience of finding relevant information.
IRJET- Customer Online Buying Prediction using Frequent Item Set MiningIRJET Journal
The document discusses using machine learning techniques like frequent itemset mining to analyze customer purchase data and predict online buying behavior. It proposes a parallel frequent itemset mining algorithm to efficiently process large datasets. The algorithm divides the data into parts, mines frequent itemsets from each part in parallel, and then combines the results. This approach improves storage capacity, computation, and performance compared to traditional frequent itemset mining algorithms. The goal is to provide useful insights into customer purchasing decisions to help businesses.
On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Vision Based Deep Web data Extraction on Nested Query Result RecordsIJMER
This document summarizes a research paper on vision-based deep web data extraction from nested query result records. It proposes a technique to extract data from web pages using different font styles, sizes, and cascading style sheets. The extracted data is then aligned into a table using alignment algorithms, including pair-wise, holistic, and nested-structure alignment. The goal is to remove immaterial information from query result pages to facilitate analysis of the extracted data.
1) This document discusses different techniques for cross-domain data fusion, including stage-based, feature-level, probabilistic, and multi-view learning methods.
2) It reviews literature on data fusion definitions, implementations, and techniques for handling data conflicts. Common steps in data fusion are data transformation, schema mapping, and duplicate detection.
3) The proposed system architecture performs data cleaning, then applies stage-based, feature-level, probabilistic, and multi-view learning fusion methods before analyzing dataset, hardware, and software requirements.
In the recent years the scope of data mining has evolved into an active area of research because of the previously unknown and interesting knowledge from very large database collection. The data mining is applied on a variety of applications in multiple domains like in business, IT and many more sectors. In Data Mining the major problem which receives great attention by the community is the classification of the data. The classification of data should be such that it could be they can be easily verified and should be easily interpreted by the humans. In this paper we would be studying various data mining techniques so that we can find few combinations for enhancing the hybrid technique which would be having multiple techniques involved so enhance the usability of the application. We would be studying CHARM Algorithm, CM-SPAM Algorithm, Apriori Algorithm, MOPNAR Algorithm and the Top K Rules.
A Hybrid Algorithm Using Apriori Growth and Fp-Split Tree For Web Usage Mining iosrjce
Internet is the most active and happening part of everyone’s life today. Almost every business or
service or organization has its website and performance of the site is an important issue. Web usage mining
based on web logs is an important methodology for optimizing website’s performance over the internet.
Different mining techniques like Apriori method, FP Tree methodology, K-Means method etc. have been
proposed by different researchers in order to make the data mining more effective and efficient. Many people
have modeled Apriori or FP Tree in their own way to increase data mining productiveness. Wu proposed
Apriori Growth as a hybrid of Apriori and FP Tree algorithm and improved FP Tree by mining using Apriori
and removed the complexity involved in FP Growth mining. Lee proposed FP Split Tree as a variant of FP Tree
and reduced the complexity by scanning the database only once against twice in FP Tree method. This research
proposes a new hybrid algorithm of FP Split and Apriori growth which combines the positives of both the
algorithms to create a new technique which provides with a better performance over the traditional methods.
The new proposed algorithm was implemented in java language on web logs obtained from IIS server and the
computational results of the proposed method performs better than traditional FP Tree method, Apriori
Method.
Dissertation data analysis in management science tutors india.com for my man...Tutors India
Management Science is very much crucial in management decision making. The primary purpose of decision-making is for effective and efficient utilization of scarce or the limited resources for which there are both private and public sectors of that economy. The present article helps the USA, the UK, Europe and the Australian students pursuing their master’s degree to identify the best data analysis, which is usually considered to be challenging. Tutors India offers UK dissertation in various Domains.
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This document summarizes an article that proposes an improved association rule mining method using correlation techniques. It integrates positive and negative rule mining concepts with frequent pattern mining to generate unique rules for classification without ambiguity. The method first constructs an FP-tree to efficiently find frequent itemsets. It then uses correlations between itemsets and class labels to determine whether rules belong to the positive or negative rule set. Classification of new data is performed by applying the matching rule subsets. The proposed method aims to address issues with traditional association rule and frequent pattern mining approaches for large datasets.
This document presents a study on finding profitable and preferable itemsets from transactional datasets. It proposes algorithms to find the top-k profitable and popular product itemsets within given utility and support threshold constraints. The paper describes the system structure, which includes modules for preprocessing product and user datasets, identifying frequent itemsets, finding smaller frequent sets, price correlation analysis, and determining the top-k popular products. An example is provided to illustrate the frequent itemset mining process. An experimental comparative study on real and synthetic datasets demonstrates the effectiveness and efficiency of the proposed algorithms.
The Best Start Project is a 7-week program that provides tools and resources to help new pastors and congregations have a successful start in their ministry together. It aims to build healthy relationships, ease grief over past leadership, gain community insights, and align on a shared vision for the church's future. The program includes sermon resources, facilitated discussions, strategic planning, and quarterly coaching. It is led by Ken Crawford and Whit Dreher and costs between $4,750-$9,500 based on congregation size.
This document discusses various methods for slope stabilization, including avoiding unstable areas, preventing instability through proper cut and fill slopes, and stabilizing existing slides. It covers the use of vegetation and drainage for prevention and stabilization, as well as structural approaches like gabions, retaining walls, reinforced fills, soil nails and anchors when stabilization is needed. The key messages are to use commonly stable slope angles, understand why failures occur, select the least costly effective stabilization measure, and ensure structures are founded on stable ground.
The document is a student paper on slope stability analysis. It was prepared by Riyaz Ahmad Bhat, a civil engineering student at the Department of civil engineering and technology. The paper discusses slope stability analysis, including the objectives of analysis, conventional methods like limit equilibrium, and numerical methods. It also describes visiting mountain slopes in Kashmir to study the effects of tectonic activity and an earthquake in 2005. The conclusion is that the paper helps understand basic concepts and procedures of slope stability analysis.
Guniting is a process that uses a cement-sand mixture projected at high pressure through a cement gun to repair damaged concrete surfaces. The mixture, usually in a 1:3 cement to sand ratio, is deposited on the surface under 20-30 N/cm^2 of pressure. Guniting can be used on vertical, overhead, and horizontal surfaces to restore concrete damaged by corrosion or inferior work. It provides an impervious layer and high compressive strength of 56-70 N/mm^2.
Group 1 presented their civil engineering project which involved designing roads, stormwater, sewer, and water networks for a site. They outlined the individual responsibilities of group members and the scope of inserting all civil services. They discussed the design standards used, constraints such as using Civil Designer software and time constraints due to student protests. The group confirmed they produced the required deliverables which included plans and longitudinal sections for stormwater, sewer, and roads designs.
The document compares the cost of designing a building using different structural systems, including a dual system without beams, building frame system with beams, and other options. It finds that the dual system without beams has the lowest total cost at $80 million, while the most expensive is the moment resisting system with beams at $120 million. Charts and tables show the cost breakdown by structural element and comparisons of total costs for each system.
This project presents an overhead bridge electromagnetic crane that can automatically measure and place objects on a conveyor belt based on their length and height. The crane uses sensors to detect objects, a microcontroller to process the measurements and control the electromagnet and motors, and can operate either automatically or manually via joystick. It is intended to help automate material handling in industries like shipping, steel mills, and petroleum refineries.
The document describes a final year project to develop a mobile and web application called SpringsVision Events for planning and managing social events. A team of 4 students - Syed Absar Karim, Umair Ahmed, Shafaq Yameen, and Zaid Hussain - presented their project to create an online platform for scheduling events, adding social networking features, and mobile support to the supervisor Mr. Nadeem Mahmood. The project aims to provide a useful tool for personal event management and sharing on social media.
Grouting involves injecting a slurry or liquid into soil or rock to fill voids and fractures. There are three main modes of grouting: permeation where grout flows freely into voids, compaction where grout remains intact and exerts pressure, and hydraulic fracturing where grout rapidly penetrates fractured zones. Grouting is used for applications like seepage control, soil stabilization, and vibration control. Common grout materials include suspensions of cement and water, emulsions of asphalt and water, and chemical solutions. Injection methods include permeation, compaction, jet, and soil fracture grouting. Proper planning of the grouting process including ground investigation, hole pattern, and sequencing is
The document discusses how social media has changed marketing and created opportunities for personal branding. It notes that 20 years ago, marketing involved newspapers and television but today focuses on social media platforms like Twitter, Facebook, and LinkedIn. The document encourages developing a personal brand on these channels, being responsive to customers, positioning oneself as a thought leader, and using one's brand to find or create the perfect job. It presents social media as a great equalizer that allows anyone to build their own future.
The document describes a series of sessions for start-ups on marketing and advertising tools. It will include 5 sessions over June and July on topics like creating a brand model, getting traffic, content creation, and data analysis tools. It also provides information on various free and paid tools for analyzing market categories, competitors, consumers, websites, and social media. Key tools highlighted include Admetricks, Statista, SimilarWeb, Ahrefs, Google Consumer Surveys, and Moz.
10 Steps of Project Management in Digital Agencies Alemsah Ozturk
This is part of our ( 41? 29! ) agency's culture series. Basicly this series of documents helps our teams learn the foundation of agency culture, basic rules to do their work. We are all about sharing the data & know how, so here we are ;)
This document discusses Flyer, a startup that aims to disrupt the commercial real estate marketing industry. Flyer wants to make the process faster, smarter, and better through the use of digital tools like web and social media. Currently a $500 billion industry, commercial real estate transactions present an opportunity for Flyer to capture part of the $30 billion spent annually on marketing through a business model that partners with brokers.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
The document describes an evaluation of existing relational keyword search systems. It notes discrepancies in how prior studies evaluated systems using different datasets, query workloads, and experimental designs. The evaluation aims to conduct an independent assessment that uses larger, more representative datasets and queries to better understand systems' real-world performance and tradeoffs between effectiveness and efficiency. It outlines schema-based and graph-based search approaches included in the new evaluation.
Algorithm for calculating relevance of documents in information retrieval sys...IRJET Journal
The document proposes an algorithm to calculate the relevance of documents returned in response to user queries in information retrieval systems. It is based on classical similarity formulas like cosine, Jaccard, and dice that calculate similarity between document and query vectors. The algorithm aims to integrate user search preferences as a variable in determining document relevance, as classic models do not account for this. It uses text and web mining techniques to process user query and document metadata.
Review on Sorting Algorithms A Comparative StudyCSCJournals
There are many popular problems in different practical fields of computer sciences, database applications, Networks and Artificial intelligence. One of these basic operations and problems is sorting algorithm; the sorting problem has attracted a great deal of research. A lot of sorting algorithms has been developed to enhance the performance in terms of computational complexity. there are several factors that must be taken in consideration; time complexity, stability, memory space. Information growth rapidly in our world leads to increase developing sort algorithms .a stable sorting algorithms maintain the relative order of records with equal keys This paper makes a comparison between the Grouping Comparison Sort (GCS) and conventional algorithm such as Selection sort, Quick sort, Insertion sort , Merge sort and Bubble sort with respect execution time to show how this algorithm perform reduce execution time.
Rule-based Information Extraction for Airplane Crashes ReportsCSCJournals
Over the last two decades, the internet has gained a widespread use in various aspects of everyday living. The amount of generated data in both structured and unstructured forms has increased rapidly, posing a number of challenges. Unstructured data are hard to manage, assess, and analyse in view of decision making. Extracting information from these large volumes of data is time-consuming and requires complex analysis. Information extraction (IE) technology is part of a text-mining framework for extracting useful knowledge for further analysis.
Various competitions, conferences and research projects have accelerated the development phases of IE. This project presents in detail the main aspects of the information extraction field. It focused on specific domain: airplane crash reports. Set of reports were used from 1001 Crash website to perform the extraction tasks such as: crash site, crash date and time, departure, destination, etc. As such, the common structures and textual expressions are considered in designing the extraction rules.
The evaluation framework used to examine the system's performance is executed for both working and test texts. It shows that the system's performance in extracting entities and relations is more accurate than for events. Generally, the good results reflect the high quality and good design of the extraction rules. It can be concluded that the rule-based approach has proved its efficiency of delivering reliable results. However, this approach does require an intensive work and a cycle process of rules testing and modification.
Rule-based Information Extraction for Airplane Crashes ReportsCSCJournals
Over the last two decades, the internet has gained a widespread use in various aspects of everyday living. The amount of generated data in both structured and unstructured forms has increased rapidly, posing a number of challenges. Unstructured data are hard to manage, assess, and analyse in view of decision making. Extracting information from these large volumes of data is time-consuming and requires complex analysis. Information extraction (IE) technology is part of a text-mining framework for extracting useful knowledge for further analysis.
Various competitions, conferences and research projects have accelerated the development phases of IE. This project presents in detail the main aspects of the information extraction field. It focused on specific domain: airplane crash reports. Set of reports were used from 1001 Crash website to perform the extraction tasks such as: crash site, crash date and time, departure, destination, etc. As such, the common structures and textual expressions are considered in designing the extraction rules.
The evaluation framework used to examine the system's performance is executed for both working and test texts. It shows that the system's performance in extracting entities and relations is more accurate than for events. Generally, the good results reflect the high quality and good design of the extraction rules. It can be concluded that the rule-based approach has proved its efficiency of delivering reliable results. However, this approach does require an intensive work and a cycle process of rules testing and modification.
Perception Determined Constructing Algorithm for Document ClusteringIRJET Journal
This document discusses an approach to document clustering called "Semantic Lingo" which aims to identify key concepts in documents and automatically generate an ontology based on these concepts to better conceptualize the documents. It provides background on challenges with traditional document clustering techniques and search engines. The proposed approach uses semantic information from domain ontologies to improve web search clustering quality by addressing issues like synonyms, polysemy and high dimensionality. It also discusses using text segments within documents that focus on one or more topics to aid multi-topic document clustering.
An Advanced IR System of Relational Keyword Search Techniquepaperpublications3
Abstract: Now these days keyword search to relational data set becomes an area of research within the data base and Information Retrieval. There is no standard process of information retrieval, which will clearly show the accurate result also it shows keyword search with ranking. Execution time is retrieving of data is more in existing system. We propose a system for increasing performance of relational keyword search systems. In the proposed system we combine schema-based and graph-based approaches and propose a Relational Keyword Search System to overcome the mentioned disadvantages of existing systems and manage the information and user access the information very efficiently. Keyword Search with the ranking requires very low execution time. Execution time of retrieving information and file length during Information retrieval can be display using chart.Keywords: Keyword Search, Datasets, Information Retrieval Query Workloads, Schema-based Systems, Graph-based Systems, ranking, relational databases.
Title: An Advanced IR System of Relational Keyword Search Technique
Author: Dhananjay A. Gholap, Gumaste S. V
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
Ontology based clustering algorithms aim to standardize clustering by incorporating domain knowledge through ontologies. They calculate similarity matrices between objects using ontology-based methods, then merge the closest clusters and recalculate the matrix in an iterative process. Several ontology based clustering algorithms are discussed, including Apriori, which generates frequent item sets to cluster data, and algorithms that use ontologies to weight features or perform recursive mining on an FP-tree. These algorithms integrate distributed semantic web data through ontologies to improve search, classification and reuse of knowledge resources.
IRJET- Determining Document Relevance using Keyword ExtractionIRJET Journal
This document describes a system that aims to search for and retrieve relevant documents from a large collection based on a user's query. It does this through three main components: keyword extraction, document searching, and a question answering bot. Keyword extraction is done using the TF-IDF algorithm to identify important words in documents. These keywords are stored in a database along with their TF-IDF weights. When a user submits a query, the system searches for documents containing keywords from the query and returns relevant results. It also includes a feedback mechanism for users to improve search accuracy over time. The goal is to deliver accurate search results quickly from large document collections.
Enhancement techniques for data warehouse staging areaIJDKP
This document discusses techniques for enhancing the performance of data warehouse staging areas. It proposes two algorithms: 1) A semantics-based extraction algorithm that reduces extraction time by pruning useless data using semantic information. 2) A semantics-based transformation algorithm that similarly aims to reduce transformation time. It also explores three scheduling techniques (FIFO, minimum cost, round robin) for loading data into the data warehouse and experimentally evaluates their performance. The goal is to enhance each stage of the ETL process to maximize overall performance.
1. The document proposes techniques to improve search performance by matching schemas between structured and unstructured data sources.
2. It involves constructing schema mappings using named entities and schema structures. It also uses strategies to narrow the search space to relevant documents.
3. The techniques were shown to improve search accuracy and reduce time/space complexity compared to existing methods.
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The goal of clustering is to group relevant documents together to help users more easily find needed information. Different clustering algorithms are reviewed, noting that hierarchical clustering and overlapping clusters may improve search results over other methods.
Analysis and Comparative of Sorting Algorithmsijtsrd
There are many popular problems in different practical fields of computer sciences, computer networks, database applications and artificial intelligence. One of these basic operations and problems is the sorting algorithm. Sorting is also a fundamental problem in algorithm analysis and designing point of view. Therefore, many computer scientists have much worked on sorting algorithms. Sorting is a key data structure operation, which makes easy arranging, searching, and finding the information. Sorting of elements is an important task in computation that is used frequently in different processes. For accomplish, the task in a reasonable amount of time efficient algorithm is needed. Different types of sorting algorithms have been devised for the purpose. Which is the best suited sorting algorithm can only be decided by comparing the available algorithms in different aspects. In this paper, a comparison is made for different sorting algorithms used in the computation. Htwe Htwe Aung "Analysis and Comparative of Sorting Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26575.pdfPaper URL: https://www.ijtsrd.com/computer-science/programming-language/26575/analysis-and-comparative-of-sorting-algorithms/htwe-htwe-aung
CONFIGURING ASSOCIATIONS TO INCREASE TRUST IN PRODUCT PURCHASEIJwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
Configuring Associations to Increase Trust in Product Purchase dannyijwest
Clustering is categorizing data into groups with similar objects. Data mining adds to complexities of clustering a large dataset with various features. Among these datasets, there are electronic business stores which offer their products through web. These stores require recommendation systems which can offer products to the user which the user might require them with higher probability. In this study, previous purchases of users are used to present a sorted list of products to the user. Identifying associations related to users and finding centers increases precision of the recommended list. Configuration of associations and creating a profile for users is important in current studies. In the proposed method, association rules are presented to model user interactions in the web which use time that a page is visited and frequency of visiting a page to weight pages and describes users’ interest to page groups. Therefore, weight of each transaction item describes user’s interest in that item. Analyzing results show that the proposed method presents a more complete model of users’ behavior because it combines weight and membership degree of pages simultaneously for ranking candidate pages. This method has obtained higher accuracy compared to other methods even in higher number of pages.
This document discusses topics related to data structures and algorithms. It covers structured programming and its advantages and disadvantages. It then introduces common data structures like stacks, queues, trees, and graphs. It discusses algorithm time and space complexity analysis and different types of algorithms. Sorting algorithms and their analysis are also introduced. Key concepts covered include linear and non-linear data structures, static and dynamic memory allocation, Big O notation for analyzing algorithms, and common sorting algorithms.
A novel approach towards developing a statistical dependent and rankIAEME Publication
The document proposes a novel approach for keyword search over XML data that consists of three main steps: 1) Indexing the XML data into two indices to facilitate searching. 2) Selecting the exact "T-type" node, which is the desired search node, using statistical formulas. 3) Performing the data search and ranking the results using a designed ranking measure. The approach aims to improve upon existing work by more effectively selecting the T-type node and ranking search results.
Applications Of Clustering Techniques In Data Mining A Comparative StudyFiona Phillips
This document discusses and compares various clustering techniques used in data mining. It begins with an introduction to data mining and clustering. It then discusses different types of clustering (hard vs soft), popular clustering methodologies like K-means, hierarchical, density-based etc. It provides examples of clustering applications. The document also discusses challenges in clustering large datasets and proposes approaches like MapReduce. It evaluates pros and cons of different clustering algorithms and their real-world applications.
This document summarizes the challenges of integrating data from different modeling and simulation (M&S) architectures used in a live-virtual-constructive simulation network. It discusses how differing data formats, representations, and structures between architectures like Distributed Interactive Simulation (DIS) and High Level Architecture (HLA) can introduce complexity. Standards, tools like gateways and the Federated Engineering Agreements Template (FEAT), and processes like the Distributed Simulation Engineering and Execution Process (DSEEP) can help address these challenges and reduce complexity when combining M&S architectures. The author recommends questioning if combining architectures is truly needed, using recognized standards, and maintaining good documentation records.
The document discusses database normalization and provides examples to illustrate the concepts of first, second, and third normal forms. It explains that normalization is the process of evaluating and correcting database tables to minimize data redundancy and anomalies. The key steps in normalization include identifying attributes, dependencies between attributes, and creating normalized tables based on those dependencies. An example database for a college will be used to demonstrate converting tables into first, second, and third normal form. Additionally, an example will show when denormalization of a table may be acceptable.
Similar to Skip List: Implementation, Optimization and Web Search (20)
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
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How to Make a Field Mandatory in Odoo 17Celine George
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Skip List: Implementation, Optimization and Web Search
1. Godwin Adu-Boateng & Matthew N. Anyanwu
International Journal of Experimental Algorithms (IJEA), Volume (5) : Issue (1) : 2015 7
Skip List: Implementation, Optimization and Web Search
Godwin Adu-Boateng gaduboateng@umc.edu
Center of Biostatistics and Bioinformatics
Department of Medicine
University of Mississippi Medical Center
Jackson, MS 39216, USA
Matthew N. Anyanwu matany58@yahoo.com
Department of Computer Science
University of Memphis
Memphis, TN 38152, USA
Abstract
Even as computer processing speeds have become faster and the size of memory has also
increased over the years, the need for elegant algorithms (programs that accomplish such
tasks/operations as information retrieval, and manipulation as efficiently as possible) remain as
important now as it did in the past. It is even more so as more complex problems come to the
fore. Skip List is a probabilistic data structure with algorithms to efficiently accomplish such
operations as search, insert and delete. In this paper, we present the results of implementing the
Skip List data structure. The paper also addresses current Web search strategies and algorithms
and how the application of Skip List implementation techniques and extensions can bring about
optimal search query results.
Keywords: Skip List, Efficient Algorithms, Web Search.
1. BACKGROUND & INTRODUCTION
One of the fundamentals in the study of computer science is the representation of stored
information. Even as computer hardware has become more sophisticated, and the size of
memory has increased over the decades and sustains an upward trajectory, the resources
required for computer programs (memory space and time) to execute implemented programs
continue to be in the fore-front. Programs that accomplish such tasks/operations as information
retrieval, and manipulation as efficiently as possible have garnered considerable attention from
computer scientists [1]. In Galil and Italiano [2], the authors’ survey data structures and algorithms
proposed as a solution for a set union problem. Weiss [3] analyzes a disjoint set problem and
provides a real world scenario of its application in a computer network. Both Galil and Italiano,
and Weiss investigate the memory space and time complexity of proposed algorithmic solutions.
Collins [4] also discusses it with the Java programming language as a back drop. Literature exists
that approach this fundamental problem in other ways. Review [5, 6, 7] for further discussion on
algorithm analysis and efficiency. To achieve optimal efficiency in data manipulation, the type of
data structure used plays an important role. Shaffer [1] defines a data structure as “…any data
representation and its associated operations.” He goes further to state that “...a data structure is
meant to be an organization or structuring for a collection of data items.” A scheme of organizing
related data items is how Levitin [7] defines a data structure. In essence, a data structure is any
organization of related data items and its associated operations. The data is organized such that
it can be used optimally. Hence, data structures are an integral part of algorithm execution, and it
can determine how efficient or otherwise an algorithm is.
Today the World Wide Web or simply the Web or its contemporary name: Social web has
become part of everyday life. Each day millions of people interact with the Web in several ways,
for instance, sending and receiving e-mails, interacting with friends and colleagues, reviewing and
2. Godwin Adu-Boateng & Matthew N. Anyanwu
International Journal of Experimental Algorithms (IJEA), Volume (5) : Issue (1) : 2015 8
rating visited restaurants, searching for travel deals and merchandise. It is anticipated that this
trend would continue [8]. Searching the internet via search engines has become quite poplar as it
usually serves as an introductory point to internet usage. In fact reports show that 84% of internet
users have used search engines [9]. Many internet search engines exists today for example,
Yahoo, Bing, Google and many other more targeted ones, like Amazon and EBay, among others.
Each has its own structure in terms of query execution, information gathering/corpus construction,
and display/presentation of the relevant pages/retrieved corpus. However, the baseline goal of
retrieving relevant information remains consistent in all search engines.
As information on the Web is forecasts to increase and more especially as big data comes to the
forefront, it is increasingly going to be essential for search engines to not only retrieve relevant
documents and Web pages, but also, be optimal in ad hoc query execution. For decades now,
Text REtrieval Conference (TREC) has remained the benchmark for Information Retrieval (IR).
The main focus of this benchmark is to test for effectiveness (accuracy) in the resulting corpus of
a search query, and not efficiency [10]. A review of the literature shows that to be the case [11].
Cao et al. [12] also examine the retrieval of handwritten document images.
As earlier stated the quest for optimality is an ongoing and important one. It is even more
important when considered in the context of the ever increasing complexity of problems that
require computer solutions. The emergence of big data: A contemporary problem/opportunity
presents varying degrees of complexity [13]. It also presents challenges in the search and
retrieval of data, and data analytics, among others. Reports suggest that Web content continues
to increase as Web usage increases [13, 14] and more users across all age grades and nations
become conversant with using it. Therefore, an efficient way for search and retrieval in the face of
this continuous increase in Web data/information is in order.
Skip List (SL) is a data structure with properties that appropriately satisfies the computer science
fundamental of stored information representation. An experiment conducted clearly shows the
desirability of using SL as the preferred structure for data processing. It can also play an
important role in Web search as it also provides a data organization scheme that yields better
efficiency in operations such as search [15].
2. SKIP LIST OVERVIEW & OPERATIONS
Skip lists (SL) was invented by William Pugh in 1989 and proposed by him in 1990. SL was
proposed as an alternative to the Binary Search Trees (BST) and other balanced trees. It is a
probabilistic data structure based on parallel linked list where elements are kept by key. But, as
opposed to linked list, a node can contain more than 1 pointer. The number of pointers in a node
determines its level. Each pointer points to the next node in the list that has an equal or greater
level. If there isn't any forward node having this property, the pointer points to NIL [16]. The level
of a Skip list corresponds to the highest level between the nodes. When a node is inserted to the
list, a node is given a random level [16]. Figure 1 provides a pictorial depiction of a SL.
FIGURE 1: Skip List. Perfect Skip List.
3. Godwin Adu-Boateng & Matthew N. Anyanwu
International Journal of Experimental Algorithms (IJEA), Volume (5) : Issue (1) : 2015 9
The main motivation for using SL include among others is that it ensures a more natural
representation than a tree, it is simple to implement, it has both practical and theoretical
applications and its efficiency is comparable to a balanced tree where the expected time for an
operation (such as searching for an element) is O(log n)1, where n is the node/random level [1,
16].
SL can be used as a replacement for other data structures. It also has other operational
efficiencies as noted by Pugh [16];
It is relatively easy to design and implement the algorithm
It is efficient
The randomization is not dependent on the input keys, thus it will be difficult for an
advisory to access using the input keys
It serves as an alternative to Binary search trees which become unbalanced after several
insertion and deletion operations
It requires a random number generator at the time of insertion
The burden on memory management is due to varying size nodes
2.1 Skip List Operations
Like any data structure, SL supports such operations as search, insert and delete.
The search for an element starts at the header on the highest level, then traverses forward
pointers that don't overshoot the element being searched for. If that happens, move down to the
next level. A brief pseudo code description of the search algorithm is shown in Figure 2.
This algorithm is very simple to implement, it searches for the right place for an element to be
inserted. Splice the list and insert the element with a random level.
Deleting/removing an element is also like inserting an element, it searches for the right place for
an element to be deleted. Splice the list and delete the element with a given random level [17].
FIGURE 2: Skip List Search Algorithm.
2.2 Practical Application of Skip List
A cursory search for practical application of the SL algorithm yielded varying levels of its
application in the real world. Some of the practical applications are as follows:
Parallel Computing: Skip List can be applied to parallel computation where insertions
and searching of items can be accomplished in different parts of the list and in parallel
without rebalancing of the data structure [18].
1 This is a notation for identifying the order of growth of an algorithm’s basic operation. In this particular case
O(log n) represents the order of growth of SL search algorithm.
1. x <- list.header
2. for i <- list.level downto 1
3. do while x.forward[i].key < searchKey
4. do x <- x.forward[i]
5. x <- x.forward[1]
6. if x.key = searchKey
7. then return x.value
8. else return failure
4. Godwin Adu-Boateng & Matthew N. Anyanwu
International Journal of Experimental Algorithms (IJEA), Volume (5) : Issue (1) : 2015 10
Operating Systems: A common problem in computer science is the blocking of resources
for processors to complete tasks. This can lead to race conditions. Skip list can be
applied to this problem with a lock-free data structure which is more efficient and it is
easy to implement [19].
Forecasting/Prediction: Ge and Stan [20] used it for predicting queries by employing Pre-
built Models (PM). The ‘closest’ PM is selected for use in the forecasting. Predicting can
be applied to scheduling, resource acquisition, and resource requirement determination.
Graphics and Visualization: Skip list-like data structure has been used to optimize the
rendering of graphics and images [21].
It is important to state here that the list provided is by no means an exhaustive one as that would
be beyond the scope of this paper.
3. EXPERIMENT
An experiment was conducted to determine the cost of searching for an element at a given
random level.
The experiments were conducted on the algorithm using the java program on different values for
p = 1/2, 1/4 and 1/8.
For each value of p, 1000 lists were randomly created for different sizes of n: 10, 100, 1000 and
10000.
For each case, the average number of steps to find 1000 random elements in the list was found.
The average is then compared to log(1/p(n)) and to L(n)/p + 1/(1-p). Then the maximum number
of steps recorded between 1000 searches was added. The result is shown in the following tables
with the different p values and different n sizes.
3.1 Results
The results shown in the proceeding section illustrates well enough the analysis of the expected
search cost.
In general, for the 3 different values of p, the average number of steps grows logarithmically to
the number of elements n. In fact, for p = ½, the average is very close to log (1/2(n)). On average,
the algorithm performs better for p = ½. But, as demonstrated in [17],
“…choosing p = ¼ slightly improves the constant of factors of the speed of the algorithm.” This
means that the algorithm is more consistent, and there is a higher probability to obtain a search
cost close to the average when choosing ¼.
Thus, the maximum number of steps is relatively low for high instances of n. For p = ½ (see Table
1), it is only 38 steps for a list of size 10000. (38 is only the maximum number of steps in the
experiment that we did, and it is possible to obtain a higher maximum). This implies that it is very
unlikely to obtain a list largely unbalanced that will give us an extremely poor performance.
n Average
Number of
Steps
Log (1/p(n)) L(n)/p + 1/(1-p) Maximum
Number of
Steps
10 3.45 3.32 8.64 10
100 6.56 6.64 15.2 26
1000 9.19 9.96 21.9 35
10000 9.92 13.29 28.5 38
TABLE 1: Average number for steps for finding 1000 random list elements. P=1/2.
5. Godwin Adu-Boateng & Matthew N. Anyanwu
International Journal of Experimental Algorithms (IJEA), Volume (5) : Issue (1) : 2015 11
n Average
Number of
Steps
Log (1/p(n)) L(n)/p + 1/(1-p) Maximum
Number of
Steps
10 4.01 1.667 7.9 10
100 8.56 3.32 14.6 45
1000 12.57 4.98 21.9 59
10000 13.63 6.64 27.9 60
TABLE 2: Average number for steps for finding 1000 random list elements. P=1/4.
n Average
Number of
Steps
Log (1/p(n)) L(n)/p + 1/(1-p) Maximum
Number of
Steps
10 4.67 1.12 10 10
100 11.43 2.21 18.8 67
1000 17.86 3.32 27.7 112
10000 19.31 14.43 36.6 116
TABLE 3: Average number for steps for finding 1000 random list elements. P=1/8.
4. WEB SEARCH: KNOWN & UNKNOWN
Not all search engines are created equal and today an online search involves Web ‘crawlers’,
‘spiders’ or ‘robots’. These are algorithms that innocuously navigate web pages and links to
gather information. They undertake the processes that make up the dimension of a web IR, and
they can provide different results set [11].
Although algorithms for web searches are mostly proprietary, research shows they rely on
inverted indexing in the processing of search requests and corpus construction. Many search
algorithms and strategies use this approach as the basis of conducting a document search [22,
23]. Inverted indexing creates a list of index terms along with a logical linked list referred to by
Grossman [10] as a posting list. Each linked list pointer references a unique term in the index,
which is created before query execution.
Again, current web search results are represented in some form or fashion. The documents
relevance to the query, the number of times the search term is found in the document, or the
interval between search terms in the document are some of the representation criteria discussed
by Li et al. [22].
As previously stated the benchmark for IR is on the effectiveness and not efficiency. Hence, there
is little to no mention of the optimality of search engine algorithms. It is our belief that an efficient
data structure for the inverted index can improve performance.
5. CONCLUSION & FUTURE WORK
SL has an expected time of O(log n) for an operation such as search or insertion. Based on the
experiments that we did in this project, we can conclude that SL is a good alternative to balanced
tree. Taking the same argument from the author, SL is very easy to implement which is the
biggest advantage over balanced trees.
Also, SL can be applied to inverted indexing; which features prominently in web searches.
Research has shown that SL, when applied to web search offers improved efficiency. Boldi and
Vigna [24] discuss embedding SL in inverted index. Inverted index search improves the efficiency
of SL by reducing the space requirement of web searches. The reduction in the space
6. Godwin Adu-Boateng & Matthew N. Anyanwu
International Journal of Experimental Algorithms (IJEA), Volume (5) : Issue (1) : 2015 12
requirement further improves the expected time of search of SL. Also inverted index ensures that
all occurrences of a query is retrieved. Chierichetti et al. [25] report on using different ways of
determining the precise locations of skips to be placed in an inverted index with an embedded SL.
They report the time complexity on an optimal algorithm as being linearithmic. Also, Campinas et
al. [26] extend the basic Skip list for a block-based inverted list and found a lower search cost.
There appears to be promise in the continued application of skip list techniques and extensions
as it pertains to search engine optimization. This is a step in the right direction when considered
in the context of current trends in web searches and document retrieval, where the amount of
documents on the web is estimated to be in the billions. Google, one the most used search
engines has over 2 billion indexed documents. The current estimate of online documents is over
500 billion today. Li et al. [22] report on the feasibility of a peer-to-peer web search. Future work
will focus on determining SL performance for TREC as well for efficiency.
6. ACKNOWLEDGEMENT
We are grateful to Serge Salan from University of Memphis TN for his contribution in
performing the Skip-list experiment.
7. REFERENCES
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