1 / 23

Collection Building Interfaces with Luna Insight

Collection Building Interfaces with Luna Insight. Gale Halpern ( geh12@cornell.edu ) Representing the Luna Development Team Mira Basara, Rick Silterra, Surinder Ghangas. Growing Image Collections. Large dynamic image collections managed in Luna Insight

rafiki
Download Presentation

Collection Building Interfaces with Luna Insight

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Collection Building Interfaces with Luna Insight Gale Halpern (geh12@cornell.edu) Representing the Luna Development Team Mira Basara, Rick Silterra, Surinder Ghangas

  2. Growing Image Collections Large dynamic image collections managed in Luna Insight • Herbert F. Johnson Museum of Art digitization project (Museum on-line) – began in 1998. • Knight Visual Resources Facility digital image collection for instruction within the Cornell Art, Architecture and Planning departments (Slide Library on-line) – began in August 2007. Smaller dynamic collections in Luna • Rare Books and Manuscript Digital Collection • New York Aerial Photographs

  3. Luna • has an ‘open’ architecture, allowing image collections to interface to collection-specific ‘source’ tables. • permits any collection-specific metadata schema which can be mapped to industry-wide standards. • is a digital delivery platform, not a repository. An interface could be built between Luna and an institutional repository.

  4. Collection Sizes

  5. Types of Collections

  6. Different Challenges faced • Where is the source data? • platform (Oracle, Access,) • commercial vs. homegrown software • Metadata schema (Dublin-Core-like vs. VRA-like (Visual Resource Assoc.)) • Data mapping between Luna and the feeder system • Workflow/coordination of manual and automated tasks • Frequency of update (once per month vs. once per week) • Data quality – whose responsibility is it?

  7. Workflow How Luna collections are created? • Metadata is catalogued by end-users. • Images are scanned from slides/books or objects photographed, then .tiffs are sent to DCAPs for processing (to build .jpeg derivatives). • Data and Images are indexed and linked.

  8. KVRF/Luna interface Library 24 Server Knight Visual Resources Facility Server TEXT FILES Works, Images, Creators, Work Relationships Uploaded TEXT FILES PicTor Access Database Data Clean-up (PERL scripts) Scanned Images (.tiffs) Luna data upload DCAPS PC with Luna Media Batch Tools Image Derivatives (.jpegs) Luna Insight Oracle Database Create Derivatives Luna Indexer CD’s containing .tiffs

  9. The Museum System(TMS)/Luna interface Bonanzap Server (CIT) Library 24 Server (DLIT) Oracle DB Link TMS Oracle Database Oracle views of TMS data Luna Insight Oracle Database Luna Indexer Photo Studio Server (Johnson Art Museum) Digital Images (.tiffs) DCAPS PC with Luna Media Batch Tools Create Derivatives Image Derivatives (.jpegs) CD’s containing.tiffs

  10. PicTor Knight Visual Resource Collection

  11. Text Files Knight Visual Resource Collection Works.txt Images.txt

  12. Data Compliance • Built PERL scripts which reconcile problems in the data • Normalize non-relational data • Consolidate data stored in redundant locations • Populate fields for Images with no Work Number • Ensure correct display sequence (i.e. multiple titles, creators, etc.) Knight Visual Resource Collection

  13. Knight Visual Resource Collection Interface – SQL View • SQL view selects data from the ‘cleaned up’ text file data. • transforms flat Pictor data to a normalized, VRA-like format. • VRA is a Visual Resource Association metadata standard

  14. Knight Visual Resource Collection

  15. Knight Visual Resource Collection

  16. The Museum System (TMS) HerbertF. Johnson Museum Collection

  17. Part 1. TMS Database – SQL View • TMS data structure is proprietary & non-compliant • View transforms TMS data to HFJ compatible data structure (Dublin Core-like) • Created one TMS view per HFJ DC-like table HerbertF. Johnson Museum Collection

  18. Part 2: Luna SQL View of a TMS SQL View • hfj.bvtitle selects from vtitle @bonanzap (the TMS server at CIT). • Results of hfj.bvtitle are loaded into hfj.bvt_table a table on the Luna server. • Luna indexer runs against the hfj.bvt_table. HerbertF. Johnson Museum Collection

  19. HerbertF. Johnson Museum Collection

  20. What’s important for future? Building future library systems: • Buying/contracting for external solutions or building blocks(Luna Insight, Artstor, The Museum System) • Use of SQL views to transform metadata and build interface. • Using building blocks and interfaces (glue) to create working systems.

  21. Some thoughts on the future • Create image collection repositories while maintaining the ability to build collections (should Luna source tables be Fedora repositories?) • Improve the building blocks (i.e. replace Pictor with an Oracle solution). • Improve the metadata (shouldn’t these all be OAI-PMH compatible?) • Migrate to real-time interfaces without human intervention.

More Related