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Deployment and Future Directions

Deployment and Future Directions. Stanford Digital Library Project Presentation by: Andreas Paepcke. Interop. Experiments. System. Deployment. Deployment Modes. Base technology. Services. Knowledge. Dissemination. $. Deployment Examples. Base technology transfer:

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Deployment and Future Directions

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  1. Deployment and Future Directions Stanford Digital Library Project Presentation by:Andreas Paepcke

  2. Interop Experiments System Deployment Deployment Modes Base technology Services Knowledge Dissemination

  3. $ Deployment Examples • Base technology transfer: • Jylu: Earliest (if not first) mobile CORBA ORB • ILU improvements • Object exchange model (OEM) • Services: • Fab (100 users): Feedback-sensitive Web retrieval agent • InterBib: Highly interoperable bibliography support

  4. Deployment Examples (cont.) • Interoperability experiments • CMU skims in SenseMaker. CMU accessing InfoBus • U. of Illinois/U. of Arizona CS Quest in SenseMaker • Berkeley accessing the InfoBus • Knowledge dissemination • Three dozen publications over three years • Weekly digital library seminar • Database workshop for Bay Area companies • Presentation to Stanford Forum industrial affiliates • STARTS • Graduating students • Many visitors

  5. System Evaluation/Deployment Strategy User Evaluation Phase I For User Evaluation Phase II: Technical/UI Improvements DLITE/Sensemaker deployment at: • Xerox PARC • Stanford University Computer Science library • NASA library

  6. User Evaluation Phase I • Questions being answered: • Usage models understandable? • Affordances natural? • Performance sufficient? • What do users do with the system? • Evaluation techniques: • Post-usage questionnaires • Log analysis • Observation • Heuristic evaluation • Interviews

  7. Phase I Evaluation Result Sampler • DLITE interface:  User model exciting and understandable  Front/backend architecture helps deployment  Need multi-threading for: - Perceived performance - Resilience to service failure/overload • SenseMaker:  Users understood and used concepts  Need better UI technologies than HTML/JavaScrip

  8. Phase I Evaluation Result Sampler (cont.) • Fab:  System performance improves over time as hypothesized  System’s profiles accurately predict user preferences  Initial from-zero learning phase not tolerable • Sound-based interfaces:  Blind users’ reactions very encouraging  Multiple speakers in a single interface is useful  Relative differences are difficult to use  Attitude/aptitude around sound in interfaces very different for blind vs. sighted users

  9. Deployment Challenges • Use of advanced technology (CORBA, Java, …) • Contractual limitations • Maintenance expectations

  10. Deployment Challenges (cont.) Stanford Student $ ilicon Valley

  11. Deployment Challenges (cont.) System features Threads Language independence PassingDictionaries Pass ByValue Platform independence

  12. Medium Term Testbed Plans • Infrastructure • Parallelism • Increased code mobility • Track evolving CORBA facilities(e.g. Netscape’s/Oracle’s/Sybase’s native ORBs) • Metadata • Value translation • Proxy status protocol • STARTS integration

  13. Medium Term Testbed Plans (cont.) • User interface • Metadata driven query construction • Event notification • Additional services, multi-input service • Query translation • Approximate translation • Additional vector space query facilities • Continue and grow new research efforts

  14. PhysicalWorld InfoBus Long Term Plans Transdu c e r s Information-BasedCollaboration Value Filtering Cellular Repository Perpetual Activity Service

  15. Problem: Service Robustness Corruption Overload Network partitioning Death/Malfunction Perpetual Activity Service

  16. Perpetual Activity Service • Monitor liveness • Check correctness • Restart • State replication • Request execution guarantees • Periodic actions • Query execution • ... Service

  17. Special Search Needs Views across media Problem: Federating Digital Libraries Very Large Scale Storage/Retrieval Distributed expertise, authority, maintenance (e.g. WWW) (e.g. NCSTRL) Broad data mining interests (e.g. MVD, Backrub) (e.g. advertising, intelligence, public health, dating) Cellular Repositories

  18. Indexing Naming IP, Revenue Reliability,Archiving ComplexObjects Storage Cellular Repositories CORBA Names CNRI Handles ... Music Film Virtual Reality ... Perpetual AS (E.g. LC) E.g.: Weather on maps ... WWW Video Maps

  19. Uniform, cross-domain Action-aware authorization info management Monitoring evolving VLS information Problem: Collaboration in Information-Intensive Environments (e.g. workflow support, process-based search) (e.g. who/when/from-where/what,action control in info webs) (e.g. link integrity,view maintenance) Information-Based Collaboration

  20. What next: • Reply • Forward • ... Information-Based View: Email Initiator What Participants When Origin Date: Wed, 16 Apr 1997 15:16:29 -0700 (PDT) From: David Maluf <maluf@DB.Stanford.EDU> Message-Id: <199704162216.PAA02829@Bigeye.Stanford.EDU> To: dbseminar@CS.Stanford.EDU Subject: CS545 DB Seminar The database group at Stanford invites you to attend the second Talk of the 1997 Spring Seminar Series this Friday (April 18) at 3:15PM.

  21. What next: • Roll back • Revise • ... Information-Based View: Source Control Participants Initiator What When Origin RCS file: /u/testbed/CVSROOT/dldev/src/interbib/bibconvert.py,v Working file: bibconvert.py total revisions: 30; selected revisions: 30 description: ---------------------------- revision 1.30 date: 1997/04/01 02:36:58; author: paepcke; state: Exp; lines: +5 -3 Fixed xlation of BibTeX \~{} idiom which inserts a ~ char. ----------------------------

  22. Information Streams Crawlers Newsfeeds Sensors Email When Origin What Action... Schema Caster • Workflow • Action-aware queries • ...

  23. Longer Term: Digital Physical World Physical World OnlineAuctions InfoExpress Digital World OCR Print OnDemand FederalExpress

  24. Direct/implied recommendations Problem: Information Efforts Wasted Access dynamics Information linkage Value Filtering

  25. Information feeds: • - Stocks • - Intelligence WebDocs Value Filtering EnterpriseDatabases Value filters: Collaborative: recommendation, bookmarks, reading lists, ...Structural: information co-location, link incidence, phrase length, ... Access statistics: access frequency, re-visitation rate, access origin Examples: Backrub, Sonia

  26. Summary InfoBus+ServicesImplemented Improvements Evaluation Phase I You R Here Improvements/deepening More publicdeployment Evaluation Phase II New projects

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