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FEDORA at Northwestern University

FEDORA at Northwestern University. Bill Parod Academic Technologies Northwestern University bill-parod@northwestern.edu. General Background. Academic Technologies Faculty projects Library partnerships Institutional partnerships Diverse clientele Diverse content “One-off” projects.

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FEDORA at Northwestern University

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  1. FEDORAatNorthwestern University Bill Parod Academic Technologies Northwestern University bill-parod@northwestern.edu

  2. General Background • Academic Technologies • Faculty projects • Library partnerships • Institutional partnerships • Diverse clientele • Diverse content • “One-off” projects Northwestern University

  3. Current FEDORA Projects • Block Museum of Art • The Last Expression Art Collection • Introduction to Asian Art History • BBC Spoken Word Archive • Paris Map Collection • Encyclopedia of Chicago • WordHoard Text Analysis Project Northwestern University

  4. Art collections Wall murals Photographs Historical maps GIS maps Newspapers Book page images Digital video Spoken word Literary works Encyclopedias Lexical data Census data Event data Diversity of Content Northwestern University

  5. Wavelet Image Servers Vector Image Processors Streaming Media Servers RDBMS XML Databases XSLT Processors GIS Servlet Engines Diversity of Systems Northwestern University

  6. Art collections Wall murals Photographs Historical maps GIS maps Newspapers Book page images Digital video Spoken word Literary works Encyclopedias Lexical data Census data Event data Abstract Image Models Northwestern University

  7. Core behavior getCoverpage getThumbnail Basic image (UVa) getThumbnail getMedium getHigh getVeryHigh Addressable image getRegion(rgn,size) getViewer Layered image getRegion(,,layers) getViewer(layers) Geographic image getRegion(,,, coords) getViewer(, coords) 4 Image Behavior Classes Northwestern University

  8. Core behavior XML Metadata HTML XSLT script Thumbnail Image Basic image (UVa) Thumbnail jpeg Medium Res jpeg High Res jpeg Very High Res jpg Addressable image Image metadata Viewer XSLT script Layered image Layer metadata Geographic image World file for projection 4 Image Content Models Northwestern University

  9. Northwestern University

  10. BDEF Interface Definition Northwestern University

  11. BMECH Description • Method bindings to implementation • HTTP URL templates to image servlet • Accepts image server metadata stream • Accepts specific user parameters • Provides implementation flexibility • Currently using TrueSpectra/Scene7 image server Northwestern University

  12. getCoverPage() for simple image – Block Museum Collection Northwestern University

  13. getCoverPage() for zoomable image – History of Asian Art class Northwestern University

  14. Ingesting Images • Imaging person deposits master TIFF images in WebDAV enabled file stor • Image server configured with “virtual path” to WebDAV stor for master image tiff. • TIFF master is converted to FlashPix and cached in image server • Image server handles request for FEDORA dissemination Northwestern University

  15. Image Workflow: FEDORA – TrueSpectra – Xythos Department Academic Technologies Users Dissemination Requests Metadata in Excel METS FEDORA TrueSpectra Image Server Data flow Requests Tiffs in Xythos • Catalog in Excel converted to METS for FEDORA ingest • Tiff Masters deposited in collection’s Xythos directory • Access to Xythos directory enabled for TrueSpectra virtual paths • METS/FEDORA record includes link to TrueSpectra image • Access to image is through FEDORA image behaviors Northwestern University

  16. Physical Collection Management Scenario: FEDORA – Content Service – Xythos Integration Faculty or Support Academic Technologies Users Dissemination Requests Files in Xythos Auto-ingester FEDORA TrueSpectra Streaming Server Search Data flow Requests Metadata update • FEDORA collection object attached to Xythos directory • Xythos notifies collection object of changes in the directory • File added – collection creates new member item • File updated – item accepts new version for file stream • File removed – item is set dormant in FEDORA • Metadata added/updated online or batch Northwestern University

  17. Collection behavior getSearchForm performSearch() getItem() getItems() addItem() deleteItem() reindex() displayItem() Core behavior getCoverpage getThumbnail Block Museum of Art The Last Expression Vesalius Figures BBC Audio History of Asian Art Basic Collection Object Northwestern University

  18. Collection Content Model • Search Form • XSLT for search results • Index • Header/footer XML for result stream • Member PIDs Northwestern University

  19. Search Implementation • FEDORA METS files currently indexed offline • Plan to integrate update notification and indexing • Search Engine • Have 3 implementations: • FEDORA native search • Sgrep • OpenText • Investigating SRW/CQL • Search results passed through XSLT • Easy to provide search capability to collections Northwestern University

  20. FEDORA – External Service External Services Cache data Dissemination Requests FEDORA Image Server Search Engine BMECH Data Request Dissemination Northwestern University

  21. Northwestern University

  22. link Northwestern University

  23. Virtual Collections • Collection maintenance • Topical galleries • Ad-hoc or dynamic collections • For classes... • personal collections… • special exhibits… Northwestern University

  24. Northwestern University

  25. Database Integration • SQL/XQuery for object “data streams” • SQL/XQuery for object disseminations Northwestern University

  26. Encyclopedia of Chicago • In active development • Metadata continually updated by research staff in Microsoft Access • New content continually added to MS Access and file stor • Varied entry types • All have dynamic “See Also”s Northwestern University

  27. SQL Datastreams • “See Also” and “Content” datastreams • Cocoon urls that perform SQL queries on dynamic research data and convert to XML. • Dynamic updates during development • When project finished will consider moving to more robust database or “freeze” streams in the repository as “managed”. Northwestern University

  28. FEDORA – External Service External Services Cache data Dissemination Requests Image Server FEDORA Search Engine BMECH Data Request Dissemination RDBMS Data stream Northwestern University

  29. WordHoard Text Analysis • Large TEI XML Etext corpora • Word level grammatical and frequency data • Text requests via Xquery • Word level lexical queries via SQL Northwestern University

  30. Basic Text Behavior BMECH Backed by eXist database Northwestern University

  31. Viewer Object Presentation uncoupled from data object Northwestern University

  32. Example Book Model Northwestern University

  33. TEXT TOC Service • Request for TOC keyed by text PID • TOC XML requested from text • TOC DOM cached in service • User requests with “open nodes” parameter • Pruned DOM styled with XSLT from Viewer content model Northwestern University

  34. Art collections Wall murals Photographs Historical maps GIS maps Newspapers Book page images Digital video Spoken word Literary works Encyclopedias Lexical data Census data Event data Abstract Text Model Northwestern University

  35. Structured text (UVa) getHeading getTOC(level) getChunk(idref) getPage(idref) Core behavior getCoverpage getThumbnail Text Methods Northwestern University

  36. Digital video Spoken word Literary works Encyclopedias Lexical data Census data Event data Art collections Wall murals Photographs Historical maps GIS maps Newspapers Book page images Time-based Media Model Northwestern University

  37. Core behavior getCoverpage getThumbnail Time-based media Play playSection() Time-based Media Behaviors Northwestern University

  38. Behaviors by Type Northwestern University

  39. Next Steps • Implement more object types • Event, video, tabular data • Transactions • Ad-hoc groupings of repository objects • Asset management, Annotation • Access control for user editing • Interoperability • Search protocols and repository interactions • Consider application models • Specialized clients Northwestern University

  40. Specialized Clients Northwestern University

  41. Viewer Object Northwestern University

  42. Summary • Code reuse through object abstraction • Flexible implementation binding • Comprehensible APIs for applications • Stable APIs for Content reuse Northwestern University

  43. Thank You Bill Parod Academic Technologies Northwestern University bill-parod@northwestern.edu

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