1. Haystack: Per-User Information Environments David Karger

2. Motivation

3. Web Search Tools Indices search by keyword Taxonomies classify by subject Cool site of the day A lot like libraries... Library catalogues Dewey digital New book shelf, suggested reading Is a universal library enough?

4. Library/web Limitations Huge Too many answers, mostly irrelevant Only published material Miss info known to few, leading-edge content Rigid All get same search results Even if come back and try again The library is the last place we look

5. Start with Bookshelf I try solving problems using my data: Information gathered personally High quality, easy for me to understand Not limited to publicly available content My organization: Personal annotations and metadata Choose own subject arrangement Optimize for my kind of searching Adapts to my needs

6. Then Turn to a Friend Leverage They organize information for their own use Let them find things for me too Shared vocabulary They know me and what I want Personal expertise They know things not in any library Trust Their recommendations are good

7. Last to Library/web Answer usually there But hard to find Wish: rearrange to suit my needs Wish: help from my friends in looking E.g. NY public library catalogue

8. Lessons Individualized access: The best tools adapt to individual ways of organizing and seeking data. Individualized knowledge: People know much more than they publish. That knowledge is useful to them and others. End user: understands their data the best, so should control organization and presentation

9. Problems with Current Tools Applications designed by few for use by many Developers decide what information is important Provide model to hold that information Provide interfaces to view/manipulate that info Users discover uses/needs for other info Tool cannot store, cannot support interaction Users discover connections between info If connected info is in different applications, neither app can record connection People could do a lot more with information, if environment let them record/use what they know

10. Haystack Approach Data Model Define rich data model that lets user represent all interesting info Rich search capabilities Machine readable so agents can augment/share/exchange info User Interface Strengthen UI tools to show rich data model to user And let them navigate/manipulate/share it Adaptation People are lazy, unwilling to “waste time” telling system what to do, even if it could help them later System must introspect about user actions, deduce user needs and preferences, and self-adjust to provide better behavior Collaboration As system gathers information from one user, share with others Rich data model maximizes useful knowledge transfer

11. Data Model A semantic web of information

12. Motivation Tremendous amount of information is relational Named relationships Written by, married to, traveling to, owned by… Collections Directories, bookmarks, menus, albums Families, workgroups, Web links People can take huge advantage of navigating relationships Network of relationships much more “structured” than a textual description, but much less regular than a spreadsheet/database

13. The Haystack Data Model W3C RDF/DAML standard Arbitrary objects, connected by named links A semantic web Links can be linked No fixed schema User extensible Add annotations Create brand new attributes Doc D. Karger Haystack title author Outstanding quality says HTML type

14. RDF Lowers Barriers Location Independent Universal Locators, even for local data (as may become non-local) Application Independent Simple, common language suitable for variety of information types Enables interlinking and exchange of information from all apps Extensible Can add attributes as needed, leave them out if unimportant Enables powerful search Based on broad variety of attributes Support for data agents Extract information from raw data Make available for search and other forms of navigation

15. Where does data come from? Pull from outside sources Web, databases, news feeds… Active user input Interfaces let user add data, note relationships Mining data from prior data Plug-in agents opportunistically extract data Passive observation of user Plug-ins to other interfaces record user actions Other Users

16. Data Extraction Services Web Observer RDF Store Mail Observer Machine Learning Services Web Viewer Haystack UI Spider

17. User Interface Uniform Access to All Information

18. Current Barriers to Information Flow Partitions by Location Some data on this computer, some on that Remote access always noticeable, distracting Partitions by Application Mail reader for this, web browser for that, text editor for those To-do list, but without needed elements Invisibility Where did I put that file? Tendency for objects to have single (inappropriate) location (folder) Missing attributes Too lazy to add keywords that would aid searching later

19. Goal: Task-Based Interface When working on X, all information relevant to X (and no other) should be at my fingertips Planning the day: to-do list, news articles, urgent email, seminars Editing a paper: relevant citations, email from coauthors, prior versions Hacking: code modules, documentation, working notes, email threads Location, source and format of data irrelevant

20. Sign of Need: Email Usage Email as to-do list Anything not yet “done” kept there Reminder email to ourselves Single interface containing numerous document types Overflowing Inboxes Navigate only by brute-force scanning Unsafe file/categorize anything: out of sight, out of mind

21. Interface Options Folders Out of sight, out of mind Still need applications to see data Which is the right folder? Desktops Allow arbitrary data types But coupling between applications & data types too light A smear of many tasks, so hard to focus Hundreds of icons, tens of windows, huge menus No partitioning Databases OK if you have a degree in database administration Interface is impoverished---long lists of tuples

22. The Big Picture

23. User Interface Architecture Views: Data about how to display data Views are persistent, manipulable data Data to be displayed UI data Mapping View Underlying information UI data Mapping 2 View 2

24. Semantic User Interface Present information by assembling different views together Information manipulation decoupled from presentation New views can be added without mucking with data types New data types can be added without designing new UIs Uniform support for features like context menus Actions apply to objects on screen in various “roles” E.g. as word, as title of mail message, as member of collection View for Favorites collection View for cnn.com View for yahoo.com View for ~/documents/thesis.pdf

25. Persistence of Views Views are data like all other data Stored persistently, manipulated by user User can customize a view View for particular task can be cloned from another Can evolve over time to need of task To an extent previously limited to sophisticated UI designer Views can be shared Once someone determines “right” way to look at data, others can benefit

26. Role of Schemata Benefits Help people look at information the right way Help creators avoid creation mistakes Risks of Enforcement Deters lazy users from entering data Prevents creative users from stretching the boundaries Is there a middle ground? Can schemata be “advisory”? One or many? If each user makes own schema, how translate?

27. Brief look

28. Adaptation Learning from the User over Time

29. Approach Haystack is ideally positioned to adapt to user RDF data model provides rich attribute set for learning In particular, can record user actions with information (the flexible UI can capture easily) Extensive record can be built up over time Introspect on that information Make Haystack adapt to needs, skills, and preferences of that user

30. Observe User Instrument all interfaces, report user actions to haystack Mail sent, files edited, web pages browsed Discover quality What does the user visit often? Discover semantic relationships What gets used at the same time? Discover search intent Which results were actually used?

31. Learning from Queries Searching involves a dialogue First query doesn’t work So look at the results, change the query Iterate till home in on desired results Haystack remembers the dialogue instead of first query attempt, use last one record items user picked as good matches on future, similar searches, have better query plus examples to compare to candidate results Use data to modify queries to big search engines, filter results coming back

32. Mediation Haystack can be a lens for viewing data from the rest of the world Stored content shows what user knows/finds useful Selectively spider “good” sites Filter results coming back Compare to objects user has found useful in the past Can learn over time Example - personalized news service

33. Collaboration Haystack’s Ulterior Motive

34. Hidden Knowledge People know a lot that they are Willing to share But too lazy to publish Haystack passively collects that knowledge Without interfering with user Once there, share it! RDF---uniform language for data exchange Challenges As people individualize systems, semantics diverge Who is the “expert” on a topic? (collaborative filtering)

35. Example I want info on probabilistic models in data mining My haystack doesn’t know, but “probability” is in lots of email I got from Tommi Jaakola Tommi told his haystack that “Bayesian” refers to “probability models” Tommi has read several papers on Bayesian methods in data mining Some are by Daphne Koller I read/liked other work by Koller My Haystack queries “Daphne Koller Bayes” on Yahoo Tommi’s haystack can rank the results for me…

36. Summary Rich data Model Lets user represent all interesting info Supports sophisticated searches Accessible to information agents User Interface Extensibly shows rich data model to user Lets them navigate/manipulate it Adaptability System may introspect about user actions, deduce user needs and preferences, and self-adjust to provide better behavior Collaboration As system gathers information from one user, share with others Rich data model maximizes useful knowledge transfer

37. More Info http://haystack.lcs.mit.edu/ (initial release available for download) karger@mit.edu