Making Research "Social" using LDAP
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Making Research "Social" using LDAP

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Making Research "Social" using LDAP Presentation Transcript

  • 1. Making research « social » with LDAP Stephan Fabel Logo
  • 2. Aloha :-) 2 Logo
  • 3. Situation  University of Hawaii System: – 10 campuses  UH Manoa campus 20,000 students  Budget cuts across the entire UH System  UHM alone >$24M in 2013   Budget 2014 for the first time 51% based on student tuition 2014 Budget for Colleges « data informed » 3 Logo
  • 4. Challenge for our College How do we capture key performance metrics for faculty and staff to support our case? 4 Logo
  • 5. COE and Symas OpenLDAP (1)   Symas OpenLDAP crucial to College infrastructure services Pre-2011: – user accounts (College-specific) – groups (College-specific)  2011-2012: – pass-through authentication to central IT – groups (POSIX and groupOfNames) local – authentication and groups in every application rolled out at the College 5 Logo
  • 6. COE and Symas OpenLDAP (2)  2013: – Publications (100%) – Grants (specification done) – Service (currently being specified)  No teaching activity stored in our directory – data available through Banner (Oracle) – but it's on the list  We run our own Student Information System which helps 6 Logo
  • 7. Capturing Research    « pet project » originally aimed at marketing efforts through public website idea was to present college research to interested third parties – Legislature – General public – Prospective students – Other researchers 7 Logo
  • 8. Public Website (1) 8 Logo
  • 9. Public Website (2) 9 Logo
  • 10. Aloha :-) 10 Logo
  • 11. Schema 11 Logo
  • 12. publications.schema ?  Dublin Core Schema: http://tools.ietf.org/html/draft-hamilton-dcxl-02  We implemented it  We didn't like it: – Distinction between authors, contributors, editors not clear enough – Everything a DirectoryString – Goal was to be able to generate APA-style citations: not possible using Dublin Core 12 Logo
  • 13. publications.schema (1)  26 attributes capturing: – Title Information, Author(s), Abstract, Type, Publisher, Volume, Pages, Owner, Venue, Location, Organization, Editor, Series, Edition, Chapter Information, Thumbnail, PDF, Month and Year – Keywords, Flag for outstanding research  8 object classes (pubObject) – Conference Proceedings, Journal Article, Book, Book Chapter, Presentation, Research Report and Multimedia Contribution 13 Logo
  • 14. publications.schema (2)   classes are auxiliary meant to be used in conjunction with the document structural object class – documentIdentifier – documentAuthor – documentLocation  For the most part, tried to keep logical attributes away from pubObject – with few exceptions 14 Logo
  • 15. Determining Author- and Ownership pubObject documentAuthor : uid=firstAuthor documentAuthor : uid=secondAuthor cn : [uidNumberFirstAuthor]XXX pubOwner : uid=firstAuthor Goal : - determine authorization to edit - only first author gets rw, all others only get r - thankfully first author never changes XXX is incrementing number Yes It's redunant :-/ Show all work from uid=sfabel : (pubOwner=uid=sfabel) Show all work where uid=sfabel was involved : (documentAuthor=uid=sfabel) Logo
  • 16. Document Identifier   cn locally unique documentIdentifier supposed to be globally unique – DOI - http://dx.doi.org/ – ISBN - http://books.google.com/   We don't want to save the publications themselves (copyright issues) We link them based on DOI through our library → paywall if not part of our system, otherwise direct access Logo
  • 17. Lessons learned / Still todo   Capture organizations as DN How to organize this in hierarchical fashion across multiple, distributed servers – Change – Federated access  Other things we're not aware of 17 Logo
  • 18. Aloha :-) 18 Logo
  • 19. Reporting API 19 Logo
  • 20. Reporting API  Written in PHP  ReST based queries (HTTP)  Binds to LDAP server and executes search  Returns data in – XML, JSON, PDF, CSV – Net file  Currently no authentication layer – Looking at possibly using OAuth 2.0 20 Logo
  • 21. Publications by Person (1) 21 Logo
  • 22. Publications by Person (2) 22 Logo
  • 23. Publications by Person (3) 23 Logo
  • 24. By Person → By Department  Using groupOfNames  Using slapo-memberof(5)   First Author is member of department → publications can be aggregated Relationship is dynamic (author moves to different department, so do his/her publications) 24 Logo
  • 25. Publications by Department (1) 25 Logo
  • 26. Publications by Department (2) 26 Logo
  • 27. Publications by Department (3) 27 Logo
  • 28. Publications by Department (4) Bonus ! 28 Logo
  • 29. Expert Search  Goal is to find the person with the highest caliber in publications around a given topic  Based on pubKeyword attribute values  Output is people (not publications!)  Ranking is performed by – Publication count, type, # of collaborators – Whether person was first author or not 29 Logo
  • 30. Keyword Search (1) 30 Logo
  • 31. Keyword Search (2) Person claims « autism » as area of interest, which guarantees being listed, but we have no publications in our system to indicate any value of his contribution. 31 Logo
  • 32. Aloha :-) 32 Logo
  • 33. So, how is it « social »? 33 Logo
  • 34. What is « social » ?  Social media: – share information with networks of people – interaction based on that shared information – goal: create « virtual community » 34 Logo
  • 35. What makes research « social » ?  Social research: – topically bounded interaction based on shared information – networks emerge through work – communities « pre-defined »: • • • • fellow researchers prospective students public/legislature administration 35 Logo
  • 36. Collaboration ↔ Interaction  Collaboration Report: – Find author pairs, calculate their “weight” – Create score based on these weights   Total relevance score average of all co-authors importance Scoped by keyword or global 36 Logo
  • 37. Collaboration Report (1) 37 Logo
  • 38. Collaboration Report (2) 38 Logo
  • 39. Collaboration Networks   Combination of expert and collaboration search Undirected graph: – Nodes people, size indicating weight – Vertices collaborative relationship, size indicating strength of collaborative efforts (number of publications, kinds of publications, number of other collaborators, etc.) 39 Logo
  • 40. Collaborative Network (example) 40 Logo
  • 41. Outlook / Future Work     Comprehensive Dashboard in planning Additional institutional research / business intelligence based on further analysis of collaboration networks Web-enabled search interfaces available to public Q1 2014 Internal reporting to be made available to all colleges, aggregation of LDAP servers to provide campus-level reporting 41 Logo
  • 42. Outlook / Future Work  Organizations: – Common thread between publications, grants, awards and service data – Will be central in future reporting  Portal for researchers: – Finding other people that you haven't collaborated with – Leveraging success of grant applications through collaboration – Providing orientation for new hires 42 Logo
  • 43. Aloha :-) 43 Logo
  • 44. Thanks! Logo