1. 1 Some of my XML/Internet Research Projects CSCI 6530 October 5, 2005 Kwok-Bun Yue University of Houston-Clear Lake

2. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 2 Content Areas of My Research Interest Some Current Projects Storage of XML in Relational Database Example Internet Computing Projects Conclusions

3. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 3 Areas of My Research Interest Internet Computing XML Databases Concurrent Programming

4. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 4 Content Areas of My Research Interest Some Current Projects Storage of XML in Relational Database Example Internet Computing Projects Conclusions

5. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 5 Some Current Projects Storage of XML in relational database Measuring Web bias using authorities and hubs Measuring information quality of Web pages Distributed computer security laboratory Collaborative Open Community for developing educational resources Generalized exchanges within organizations

6. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 6 Some Recent Student Work McDowell, A., Schmidt, C. & Yue, K., Analysis and Metrics of XML Schema, Proceedings of the 2004 International Conference on Software Engineering Research and Practice, pp 538-544, Las Vegas, June 2004. Yang A., Yue K., Liaw K., Collins G., Venkatraman J., Achar S., Sadasivam K., and Chen P., Distributed Computer Security Lab and Projects, Journal of Computing Sciences in Colleges. Volume 20, Issue 1. October 2004. Yue, K., Alakappan, S. and Cheung, W., A Framework of Inlining Algorithms for Mapping DTDs to Relational Schemas, Technical Report COMP-05-005, Computer Science Department, the Hong Kong Baptist University, 2005, http://www.comp.hkbu.edu.hk/en/research/?content=tech- reports. http://www.comp.hkbu.edu.hk/en/research/?content=tech- reports

7. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 7 Content Areas of My Research Interest Some Current Projects Storage of XML in Relational Database Example Internet Computing Projects Conclusions

8. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 8 Storing XML in RDB Advantages: –Mature database technologies. –May be queried by XML technology: e.g. XPath, XQuery. RDB technology: e.g. SQL. Disadvantages: –impedance mismatch: XML and relations are different data models.

9. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 9 Related Issues Effective mapping XML DTDs (~ ordered tree model) to relational schemas. Mapping of XML queries (e.g. XQuery) to RDB queries (e.g. SQL). Mapping of RDB query results back to XML format.

10. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 10 Related Work and Context Mapping –With or without schemas for XML. –With or without user input. Schemas for XML: –Document Type Definition (DTD) –XML Schema We consider mapping with DTD and without user input.

11. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 11 Naïve Mapping An XML element is mapped to a relation. Example 1a: XML: hello -> Relations: a, b, c and d.

12. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 12 Problems of Naïve Mapping Many relations. Ineffective queries: multiple query joins. Example 1b: XPath Query: //a SQL Query: need to join the relations a, b, c and d.

13. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 13 Inlining Algorithms First proposed by Shanmugasundaram, et. al. Expanded by Lu, Lee, Chu and others. Extended in various directions by various researchers, e.g., –Preserving XML element orders. –Preserving XML constraints. Do not consider extensions here.

14. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 14 Basic Idea of Inlining Algorithms Inline child element into the relation for the parent element when appropriate. Different inlining algorithms differ in inlining criteria. Example 1c: XML: hello Inlined Relation: a.

15. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 15 Inlining Algorithms Child elements & attributes may be inlined. Child elements may not have their own relations. Results in less number of relations. In general, more inlining -> less joins.

16. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 16 Inlining Algorithm Structure 1. Simplification of DTD. 2. Generation of DTD graphs 3. Generation of Relational Schemas

17. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 17 Our Preliminary Results 1. A more complete and optimal DTD Simplification Algorithm 2. A generic DTD Graph that can be used by inlining algorithms. 3. Inlining Considerations: framework for analyzing inlining algorithm 4. A new and aggressive inlining algorithm

18. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 18 Examples of Our Work Use DTD Simplification as an example of the flavor of our work. Show the new Inlining Algorithm.

19. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 19 Brief Introduction to DTD DTD: a simple language to describe XML vocabulary: –Element declarations: contents of elements. –Attribute declarations: types and properties of attributes. DTD is still very popular.

20. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 20 DTD Element Declarations Define element contents: –#PCDATA: string –ANY: anything go –EMPTY: no content (attributes only) –Content models: child elements. –Mixed contents: child elements and strings.

21. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 21 DTD Example Example 2: A complete DTD

22. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 22 Operators for Element Declaration,: sequence +: 1 or more *: 0 or more ?: optional; 0 or 1 |: choice (): parenthesis

23. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 23 Simplification of DTD Mapping of DTD to Relational Schemas: –Input: DTDs –Output: Relational Schemas DTD can be complicated => simplification. Example 3:

24. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 24 Simplification Principles The relational schema needs to store all possible scenarios. Some relations/columns may not be populated in some instances. Example 3: and : May be the same from the RDB’s point of view.

25. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 25 Simplification Details Comma-separated clauses: only operators remain: (),, and *. –+ -> *, e.g. a+ -> a*. –Removal of | and ?, e.g. (a|b?) -> (a,b) –Removal of (), e.g. (a, (b)) -> (a,b) –Removal of repetition, e.g. (a, b, a) -> (a*, b) Note that element orders are not preserved.

26. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 26 Previous Simplification Results Not complete: e.g. –Shanmugasundaram: not specify how to handle |. –Lu: not specify how to remove (). Not optimal (may generate * when it is not needed). Example 4a: For Lu and Lee, 2 steps: (b|(b,c)) -> (b,b,c) -> (b*,c)

27. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 27 Our Simplification Algorithm A set of definitions. A set of 7 simplification rules. An algorithm on how and when to use them. Example 4b: For us, 1 step: (b|(b,c)) -> (b,c)

28. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 28 Simplification Rules

29. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 29 Simplification Algorithm

30. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 30 Complexity Time complexity = O(N op ) Where N op is the total number of operators (including parentheses) in the element declarations of the DTD.

31. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 31 Advantages Complete: handle all DTDs. Optimal: in the sense that * will not be generated if not needed. Example 5: =>

32. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 32 A New Inlining Algorithm (1) Aggressive in inlining. More complete. Elaborated algorithms. Handle more details: e.g. element types of ANY, EMPTY and mixed contents.

33. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 33 A New Inlining Algorithm (2)

34. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 34 A New Inlining Algorithm (3)

35. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 35 A New Inlining Algorithm (4)

36. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 36 Main Results Yue, K., Alakappan, S. and Cheung, W., A Framework of Inlining Algorithms for Mapping DTDs to Relational Schemas, Technical Report COMP-05-005, Computer Science Department, the Hong Kong Baptist University, 2005, http://www.comp.hkbu.edu.hk/en/resear ch/?content=tech-reports. http://www.comp.hkbu.edu.hk/en/resear ch/?content=tech-reports

37. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 37 Future Works Implemented the algorithms and tested with many DTDs. Need to implement the XQuery/SQL bridge for performance study.

38. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 38 Content Areas of My Research Interest Some Current Projects Storage of XML in Relational Database Example Internet Computing Projects Conclusions

39. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 39 Measuring Web Bias Search engines dominate how information are accessed. Search results have major social, political and commercial consequences. Are search engines bias? How bias are them?

40. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 40 Previous Works To measure bias, results should be compared to a norm. The norm may be from human experts. Mowshowitz and Kawaguchi: the average search result of a collection of popular search engines as the norm.

41. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 41 Mowshowitz and Kawaguchi

42. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 42 Limitations Based on URL Vector -> cannot measure bias quality.

43. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 43 Our Approach Use Kleinberg’s HITS algorithm to create clusters, authorities and hubs of the result norm URLs. Use them as norm clusters, authorities and hubs. Measure distances between norms and individual results as bias.

44. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 44 HITS Obtain a directed graph G where –Node: page –Edge: URL link from between pages. Two indices: x p,i (authority) & y p,i (hub) Iterate until steady state: –x p,i+1 p y q,i –y p,i+1 q x q,i

45. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 45 Our Approach

46. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 46 Current Progress Implemented previous results. Implemented vector analysis Implemented HITS algorithm, but it is not accurate enough: –‘Conglomerate’ effect.

47. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 47 Measuring Page’s Information Quality People find information from Web pages. How good is the content of a given page?

48. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 48 Previous Works Measuring different kinds of quality: –Web site design quality –Navigational quality Many framework on how to measure information quality: –Most results in surveys so users can rank informational quality. –Very few automated or semi-automated tool.

49. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 49 Our Objectives Build automated and/or semi-automated tool to measure and/or assist user to measure information quality of a Web page.

50. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 50 Approach Hypothesis, measure, usage guidelines. Example: –Hypothesis: a Web page with many spelling mistakes is likely to have low information quality. –Measures: Show frequencies of word occurrences. Show percentage of spelling ‘mistakes’. –Usage guideline: Spelling ‘mistakes’ may not be actual mistakes (e.g. UHCL).

51. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 51 Metrics Many potential metrics. Some examples: –Broken links –HTML Quality –Domain names –Page ranking and popularity –Appearance in directory structure –History (e.g. Way back machine) –Currency (e.g. last modified) –Author (e.g. Meta tag)

52. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 52 Current Progress ‘Pre-alpha’ prototype: http://dcm.cl.uh.edu/yue/util/pageInfo.pl http://dcm.cl.uh.edu/yue/util/pageInfo.pl A capstone project

53. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 53 Content Areas of My Research Interest. Some Current Projects Storage of XML in Relational Database Example Internet Computing Projects Conclusions

54. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 54 Conclusions Good time to do applied computing research in the Web and XML areas. Style: hands-on supervision + publications. Don't forget to donate a scholarship to the School if your future research leads to a windfall.

55. 10/5/2005Bun Yue: yue@cl.uh.edu, http://dcm.uhcl.edu/yueslide 55 Questions? Any Questions? Thanks!