1. Collection Building Interfaces with Luna Insight Gale Halpern (geh12@cornell.edu)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 1.Herbert F. Johnson Museum of Art digitization project (Museum on-line) – began in 1998. 2.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 3.Rare Books and Manuscript Digital Collection 4.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.

6. Number of Digital Images (October 2007) Anticipated Total number of Images Current Image Rate of growth Herbert F. Johnson Art Museum collection 21,33936,000 +100 per month Knight Visual Resources Collection 16, 359unlimited600 per month Collection Sizes

7. Image Content (mainly) Maximum Viewable Image Resolution Copyright Herbert F. Johnson Art Museum collection Museum Objects (Permanent Collection) 24,576 pixels (lengthwise) Public Domain except post-1923 (restricted) Knight Visual Resources Collection Scans of books, slides, other sources used for instruction. 1,536 pixels (lengthwise) Restricted Types of Collections

8. 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?

9. 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.

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

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

12. Knight Visual Resource CollectionPicTor

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

14. 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

15. 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 Knight Visual Resource Collection

18. The Museum System (TMS) Herbert F. Johnson Museum Collection

19. 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 Herbert F. Johnson Museum Collection

20. 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. Herbert F. Johnson Museum Collection

22. 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.

23. 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.