Workshop

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Workshop

  1. 1. Integrating Web Data in Mashups’ Way<br />网格组07硕:卢 宾<br />
  2. 2. Outline<br />
  3. 3.
  4. 4. 6<br />计算机领域的定义<br />
  5. 5. 地图Mashup(Google Map, Virtual Earth, Yahoo Map)<br />分析大量有关事物和行为的数据中的位置信息,从而将它们在地图上形象的显示;<br />视频和图像Mashup(Flickr, Youtube, Facebook)<br />图像、视频相关的元数据(例如谁拍的照片,照片的内容是什么,在何时何地拍摄的等等),将其与其它源混搭(地图、新闻等);<br />搜索和购物Mashup<br />Amazon, eBay API的发布<br />新闻Mashup<br />个性化的报纸(联合纽约时报、BBC等数据源)<br />Mashup应用程序的类型<br />ChicagoCrime.org<br /> Amazon eCommerce + AOL Instant Messenger + Google Maps + Google Search + NOAA Weather Service +…Upcoming.org + Yahoo Local Search +<br /> Yahoo Search + Yahoo Traffic + YouTube <br />
  6. 6. 8<br />Mashup创建过程<br />1、想到一个Mashup应用场景;<br />(欣赏世界各地的海滩美景):Flickr、WindowsLiveSpace、Facebook;<br />2、阅读它们的说明文档,学习如何使用这些API;<br />3、理解各个API之间的关系,判断如何组合这些API;<br />4、编写代码进行实现.<br />
  7. 7.
  8. 8. 10<br />Yahoo! Pipes<br />
  9. 9. 11<br />Microsoft Popfly<br />
  10. 10. 12<br />IBM Mashup Center<br />
  11. 11. 13<br />Google Mashup Editor<br />
  12. 12.
  13. 13. 想到一个Mashup应用场景;<br />(欣赏世界各地的海滩美景):Flickr、WindowsLiveSpace、Facebook;<br />阅读它们的说明文档,学习如何使用这些API; <br />理解各个API之间的关系,判断如何组合这些API; <br />编写代码进行实现.<br />存在的问题<br />1、面对大量的API,如何帮助用户快速的掌握它们,并理清它们之间的关系?<br />2、如何基于用户Mashup的应用场景,为用户提供可行性的建议,从而使得创建出的Mashup应用程序内容更丰富?<br />
  14. 14.
  15. 15. Autocompletion for Mashups (ICDE09; VLDB09)<br />Wishful Search: Interactive Composition of Data Mashups (WWW08)<br />Mashmaker: mashups for the masses (SIGMOD07)<br />Damia: data mashups for intranet applications (VLDB07; SIGMOD08)<br />相关论文<br />
  16. 16. Autocompletion for Mashups<br />The work:<br />Given a user’s partial mashup specification(i.e. user select some mashlets (APIs)), recommend top-k GPs (completed mashups) that are potentially most relevant to the user’s current needs. (A GP is a mashup developed by some user.)<br />How to instantly retrieve the most relevant GPs from thousands of GPs (repository).<br />
  17. 17. Autocompletion for Mashups<br />User Input: Recommendation List:<br />Flickr<br />GoogleMap<br />Flickr<br />VirtualEarth<br />Flickr<br />GoogleMap<br />Flickr<br />GoogleMap<br />Facebook<br />Facebook<br />Youtube<br />GoogleMap<br />
  18. 18. The Approach<br />Key Observation:<br />mashups developed by different users, in similar contexts, typically share common characteristics, i.e., they use similar classes of mashup components and glue them together in a similar manner.<br />
  19. 19. A D-dimensional Space<br />D = |M| + 1;<br />A candidate g is mapped to a point pg in this<br />space with the following coordinates:<br />
  20. 20. The Algorithm<br />1.<br />
  21. 21. Computing Importance<br />The style of PageRank algorithm<br />
  22. 22. 2.Wishful Search : Interactive Composition of Data Mashups<br />allows users to explore the space of potentially composable data mashups and preview composition results as they iteratively refine their “wishes”.<br />the users express their composition requirements as tag queries describing the desired flow output.<br />
  23. 23.
  24. 24.
  25. 25. Core Ideas of sMash<br />28<br /><ul><li>1. Construct and visualize a Web of Data APIs to enable Mashup building.
  26. 26. Link Mashupable Data APIs;
  27. 27. 2. Provide recommendations.
  28. 28. Links which may make their mashupsmore abundant.</li></li></ul><li> A Screenshot of sMash<br />29<br />
  29. 29. Unique Features of sMash<br />28<br /><ul><li>1. Automatic Generation of Mashup Graph;
  30. 30. users who have clear ideas about which data APIs to use
  31. 31. fuzzy-match-keyword-search
  32. 32. 2. Surf and Mashup; (a Web of Data APIs)
  33. 33. users who have no clear purpose of mashup results.
  34. 34. leave a trace
  35. 35. 3. Trace-based Recommendation;
  36. 36. 4. Inference-based Recommendation.
  37. 37. Power users
  38. 38. 4/5 data APIs are rarely used
  39. 39. only 30% links of the network are covered.</li></li></ul><li>Architecture of sMash<br />30<br />
  40. 40. Key Technologies – Network Construction<br />31<br />Precise Representation of Data API Metadata;<br />incorporate rich semantics, rdf model<br />microformats-like semantic data types<br />Integrity of Links;<br />provide a better platform for users to exert their imagination<br />data content based matching<br />Scalability of Network.<br />take advantage of social community<br />two auxiliary user friendly tools, API schema editor and data type editor<br />
  41. 41. Key Technologies – Data Handler<br />32<br />
  42. 42. Key Technologies – Link Recommendation<br />33<br />
  43. 43. Evaluation – Trace-based Recommendation<br />34<br />Quality of Trace-based <br />Recommendation<br />Recommender Performance<br />
  44. 44. Evaluation – Inference-based Recommendation<br />35<br />(a) (b) (c)<br />Quality Evaluation of Inference-based Recommendation. (a) shows the sample data API network composed of 112 APIs and 3814 links; (b) is composed of the 112 APIs and all the links that have been used by users to build mashups so far, only 1123 links in all; (c) is a combination of (b) and the recommended links which are obtained by taking part of each mashup in repository as input to simulate users’ traces, 2288 links in all.<br />
  45. 45. 已取得的成果<br />36<br /><ul><li>1. A Mashup System: sMashV2.0
  46. 46. http://www.dart.zju.edu.cn/mashup/
  47. 47. 2. Publication:
  48. 48. Bin Lu, Zhaohui Wu, Yuan Ni, GuotongXie, Chunying Zhou, Huajun Chen. sMash: Semantic-based Mashup Navigation System for Data API network. In Proceedings of the 18th World Wide Web Conference (WWW2009), Madrid, Spain, April 2009.
  49. 49. Huajun Chen, Bin Lu, Yuan Ni, GuotongXie, Chunying Zhou, Zhaohui Wu. Mashup by Surfing a Web of Data APIs. In Proceedings of the 35th International Conference on Very Large Data Bases (VLDB2009), Lyon, France, August 2009.
  50. 50. Chunying Zhou, Bin Lu, Yuan Ni, GuotongXie, Huajun Chen, Probabilistic Semantic Learning Framework for Web Mashup Candidate Recommendation. ISWC 2009, Submitted</li></li></ul><li>
  51. 51. SIGMOD & 语义搜索引擎<br />38<br />卢宾 朋友<br />1、SIGMOD 2010;<br />2、Mashup的应用是数据集成的一种方式,同时它是一种Deep Web,搜索引擎还不能搜索到这种经过多个数据源聚合之后形成的有意义的信息;<br />同时,现在的Mashup还只是个人行为,包括我们现在做的工作也是更好的帮助用户创建Mashup,但更有意义的是如何将这些聚合后的数据共享,这将是我们以后研究的重点。<br />语义映射<br />执行引擎<br />
  52. 52. 谢谢!<br />

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