Ubiquitous Metainformation and the W Y W W Y W I Principle

1. Ubiquitous Metainformation and the W Y W W Y W I Principle Michael Bieber*, Joe Catanio*, Li Zhang** *Information Systems Department **Computer Science Department College of Computing Sciences New Jersey Institute of Technology http://web.njit.edu/~bieber November 2003 This talk ties together much of our current research.. It also gives a vision of where the WWW is heading. 1

2. Thinking Exercise • Close your eyes. Think about your favorite Web site, or one that you rely on for information... • Focus on an element you’re interested in • a text string, icon, element within a table or an animation... 2

3. The W Y W W Y W I PrincipleWhat you want, when you want it Wanting to point to something and say: • Tell me more about this! • What is this? • How can I use this? What do I need to know to use it? • Can I modify this? • How does this differ from similar ones? • What is the next step? This is all metainformation & people should get it! 3

4. Ubiquitous Metainformation Goal: Metainformation widespread in everyday systems How: provide tools for developers • Relationship Analysis • systematically determining metainformation • Metainformation Engine • automatically generating metainformation • WYWWYWI • widespread accepted design philosophy 4

5. Examples Metainformation (what to provide) Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Virtual Documents (many real world documents) Related Work WYWWYWI (what it will take) Outline 5

6. Two Examples • Purchasing System • Digital Library • screen shot of our prototype later... 6

7. Sample Screen from Purchasing System: All text with no links... 7

8. But we could want metainformation about almost any element... 8

9. V0000304390 {vendor} Vendor Details{Vendor IS} Vendor Reliability{Vendor IS} Vendor Agreements{Vendor IS} Other Possible Vendors{Purchasing Data Warehouse} Your Purchasing History{Purchasing IS} All Screens with this Vendor{CASE Workbench} 9

10. Author and Document Owner {Metadata Repository} Define this concept {Agricultural Thesaurus} 10

11. Metainformation • The full context about and around an element • Provides a fuller understanding relationships metadata element 11

12. Roberto Galnares’ dissertation Metainformation • metadata(about selected element) • content relationships (based on display content) • structural relationships(based on element type or “class”) • annotation relationships(user-declared, knowledge-sharing) • metainformation-based navigation(user-directed) 12

13. Author and Document Owner {Metadata Repository} Define this concept {Agricultural Thesaurus} 13

14. Author and Document Owner {Metadata Repository} metadata content relationship structural relationships annotation Define this concept {Agricultural Thesaurus} content relationship 14

15. V0000304390 {vendor} Vendor Details{Vendor IS} Vendor Reliability{Vendor IS} Vendor Agreements{Vendor IS} Other Possible Vendors{Purchasing Data Warehouse} Your Purchasing History{Purchasing IS} All Screens with this Vendor{CASE Workbench} all are structural relationships 15

16. Examples Metainformation (what to provide) Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Virtual Documents (many real world documents) Related Work WYWWYWI (what it will take) Outline 16

17. Joe Catanio’s dissertation Relationship Analysis (RA) • What metainformation could we provide? • RA: a systematic methodology to determine relationships (& metadata and new destination elements) • New systems analysis technique • Fills a major hole in software engineering • Analysts gain deeper understanding of a system • Yields richer analyses and designs • Relationships become links 17

18. Relationship Analysis (RA), cont. • approach: brainstorming with domain experts • for existing systems: • pick elements from screen shots • for new systems: • pick entities from use cases • Ask questions from RA taxonomy 18

19. RA Taxonomy • based on Guilford’s Structure of Intellect theory [1950] • describing intellect and creativity • refined by Rao & Turoff’s Hypertext Morphology [1991] • for systems analysis 19

20. RA Taxonomy 20

21. RA Taxonomy 21

22. RA: Brainstorming Questions RA Template 22

23. Examples Metainformation (what to provide) Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Virtual Documents (many real world documents) Related Work WYWWYWI (what it will take) Outline 23

24. Metainformation Engine • “Just in time” metainformation • required for virtual documents (e.g., query results) • Automatically: • generates link anchors • generates links to services providing metainformation: • metadata, content, structural, annotation relationships • incorporates metainformation-based navigation • Provides lightweight systems integration through linking to everyday systems Roberto Galnares’ dissertation 24

25. V0000304390 {vendor} Vendor Details{Vendor IS} Vendor Reliability{Vendor IS} Vendor Agreements{Vendor IS} Other Possible Vendors{Purchasing Data Warehouse} Your Purchasing History{Purchasing IS} All Screens with this Vendor{CASE Workbench} 25

26. User’s Web Browser Metainformation Engine ME Desktop ME Relationship Engine ME Broker ME Lexical Analysis Vendor IS Wrapper Purchasing D.W. Wrapper Purchasing IS Wrapper CASE Workbench Wrapper Service Wrapper (i) Vendor Information System Purchasing Data Warehouse Purchasing Information System CASE Workbench Service (i) existing system or Web service To Integrate: (1) wrapper: parses screens to identify elements (2) provide metadata/structural rel’ship rules (3) identify glossaries for content relationships uses Java, XML, Xpath, etc. 26

27. User’s Web Browser Metainformation Engine ME Desktop ME Relationship Engine ME Broker ME Lexical Analysis Vendor IS Wrapper Purchasing D.W. Wrapper Purchasing IS Wrapper CASE Workbench Wrapper Service Wrapper (i) Vendor Information System Purchasing Data Warehouse Purchasing Information System CASE Workbench Service (i) To Integrate: (1) wrapper: parses screens to identify elements (2) provide metadata/structural rel’ship rules (3) identify glossaries for content relationships uses Java, XML, Xpath, etc. existing system or Web service 27

28. V0000304390 {vendor} Vendor Details{Vendor IS} Vendor Reliability{Vendor IS} Vendor Agreements{Vendor IS} Other Possible Vendors{Purchasing Data Warehouse} Your Purchasing History{Purchasing IS} All Screens with this Vendor{CASE Workbench} Relationship Rules • element type (“vendor”) • link display label(“Vendor Details”) • relationship metadata for filtering links • semantic relationship type (“elaboration”) • relationship keywords • destination system(“Vendor Info System”) • exact command(s) for destination system(“retrieve_full(ID, details)”) • conditions • user types and tasks, expertise required, access restrictions 28

29. Relationship Rules • Mechanism for implementing access to: • Metadata • Structural relationships • Content relationships • Annotation relationships • Metainformation navigation 29

30. Metadatum Rule • element type( “vendor”) • metadatum link display label(“Vendor Name”) • metadatum’s metadata for filtering links • semantic relationship type (“name”) • metadatum keywords • destination system (“Vendor Info System”) • exact command(s) for destination system(“select(vendor_table, vendor_ID, vendor_name)”) • conditions • user types and tasks, expertise required, access restrictions 30

31. Annotation Rule(display comments) • element type( “generic_element”) • link display label(“view comments on this”, element_type)”) • relationship metadata for filtering links • semantic relationship type (“annotation”) • destination system (“Annotation System”) • exact command(s) for destination system(“display_annotations(element_ID)”) • conditionscheck_condition(“Core Annotation Service”, existence_check(“annotations”, element_ID)) = true) 31

32. Prototype:Digital Library Integration 32

33. Interesting Issues • Information overload! • Must filter and rank order list of links • Too many anchors • Requires good user interface design • Semantics • Systems/services should use same element types • Unique persistent identifiers • For every screen, document, element of interest 33

34. Examples Metainformation (what to provide) Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Virtual Documents (many real world documents) Related Work WYWWYWI (what it will take) Outline 34

35. Virtual Documents • from user interaction, queries, customizations • Metainformation must be added “just in time” • Example • do a decision support analysis (“# vehicles needed”) • add comments to calculation results • bookmark screen (“make it a favorite”) • close screen • follow bookmark later (“system regenerates screen”) • system must re-locate comment anchors “just in time” 35

36. Li Zhang’s dissertation Virtual Documents • Re-generate virtual documents • without re-entering parameters • then wrapper parses to add metainfo anchors • Re-identify elements • Location can shift • content can change (e.g., stock price) • Re-locate anchors 36

37. Examples Metainformation (what to provide) Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Virtual Documents (many real world documents) Related Work WYWWYWI (what it will take) Outline 37

38. Related Work • Web Services • Semantic Web • Microsoft SmartTags/NBC-Interactive’s QuickClick, etc. • Link Services and Hypermedia Engines 38

39. Web Services • The Metainformation Engine (ME) is a kind of Web service • a metainformation/linking service • ME can integrate with existing Web services • include Web services in set of links • supplement Web services with links 39

40. Semantic Web • Advocates the use of ontologies (groups of related terms) to define concepts and their relationships • Goal: machine-readable semantic description / processing • Recognizes the need to accommodate diverse knowledge representations and conflicting definitions. • The ME could use ontologies to: • standardize element types • Find related elements and provide links to their metainformation 40

41. Microsoft SmartTags / NBC-Interactive’s QuickClick, etc. • Set of links generated automatically • Based on known keywords • Can add additional links manually • No structural linking 41

42. Microsoft Smart Tag critiques • Users find too many links annoying • Content providers do not want their documents altered • Manipulation: Companies can pay to have competitors name recognized and linked to their sites • Provides content based links (word recognition) not structural linkages (element recognition) 42

43. Link Servicesand Hypermedia Engines • SFX (links to referenced articles) • Link Services • Chimera, Microcosm Distributed Link Service, DHM: manual linking or automated through keyword search requiring modifications for integration • Hypermedia Engines with minimal modifications • Microcosm Universal Viewer, Freckles: manual linking • OO-Navigator: SmallTalk only • Web Database Applications, e.g., e-shopping • database queries only, normally single links 43

44. Examples Metainformation (what to provide) Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Virtual Documents (many real world documents) Related Work WYWWYWI (what it will take) Outline 44

45. What you want, when you want itWhat will it take? • WYWWYWI mindset for developers & public • Allow metainformation (user-directed) navigation • a design philosophy for developers • demanded by the general public • this requires exposure! • Developer Tools • Ubiquitous Access 45

46. What you want, when you want itWhat will it take? • WYWWYWI mindset for developers & public • Developer Tools • Relationship Analysis • Metainformation Engine • Wrappers for everyday systems • Annotation/knowledge-sharing services (linking, comments, guided tours, etc.) • Ubiquitous Access 46

47. What you want, when you want it:What will it take? • WYWWYWI mindset for developers & public • Developer Tools • Ubiquitous Access • Repositories of relationship rules • Thesauri and glossaries 47

48. Interesting Issues • Access privileges to others’ metainformation • Right to third party authoring/linking • Quality of metainformation • Rating metainformation • Bogus metainformation (such as advertisements) 48

49. Metainformation broader conceptualization Relationship Analysis (how to find metainformation) Metainformation Engine (how to automate it) Lightweight systems integration through linking Virtual Documents Re-generation, re-identification, re-location WYWWYWI: a design philosophy What you want, when you want it Research Contributions Thank you! Questions, please? 49