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Metadata and Cross-Collection Searching in Luna’s Insight

Metadata and Cross-Collection Searching in Luna’s Insight. Problem: Integrating Access to Visual Collections. Diverse visual resources and descriptions Multiple repositories at Cornell, multiple digital collections, distributed digital collections

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Metadata and Cross-Collection Searching in Luna’s Insight

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  1. Metadata and Cross-Collection Searching in Luna’s Insight Cornell Institute for Digital Collections

  2. Problem: Integrating Access to Visual Collections • Diverse visual resources and descriptions • Multiple repositories at Cornell, multiple digital collections, distributed digital collections • Different discovery methods and metadata formats • Searchers are on their own to be aware of collections, know how to link to them, and search different interfaces Cornell Institute for Digital Collections

  3. 1st Solution: A Shared Union Catalog for Images • Adopted MultiMIMSY 2000 from Willoughby Associates • Museum collections management software • Moved data from stand-alone applications into it • For the past 4 years, have worked on developing shared standards and practices Cornell Institute for Digital Collections

  4. MIMSY Demo Cornell Institute for Digital Collections

  5. Standards Issues • No museum descriptive standard • CIDOC reference framework as a glue? • We have tried to follow VRA 3.0 • Use AAT, ULAN, TGN, for data values Cornell Institute for Digital Collections

  6. Is VRA 3.0 too complex? • [example] Cornell Institute for Digital Collections

  7. 2nd Integration Solution: Insight from Luna Imaging • Addresses issues of collection diversity • Can search multiple collections at once • Addresses issues of metadata diversity • Maps data to a common standard • Allow searching across multiple heterogeneous collections Cornell Institute for Digital Collections

  8. Insight Demo • Selected features: • General search and display attributes • Cross-collection searching • Variable metadata displays • Annotation tool • Support for formats Cornell Institute for Digital Collections

  9. Insight’s support of descriptive complexity • Controlled vocabulary lists and repeating values for fields; • Hierarchical structures and values; • Groups of fields that should be treated together, e.g., artist name, life dates, nationality; • Display order of values, fields, and groups of fields Cornell Institute for Digital Collections

  10. Cornell Institute for Digital Collections

  11. Insight v3 Data Structure • Replicates source data in a format common to all Insight collection databases Values Terms Inverted Index Tables Tables Joins Fields FieldGroups Mapping Tables Objects People Location Location Hierarchy Events Source Data Tables

  12. Collection Manager Virtual Collection A Virtual Collection B Virtual Collection C Virtual Collection D Repository A Repository B Repository C Insight Virtual Collection Manager

  13. StandardID StandardName FieldID DisplayName MappingStandard MappingStandardFieldID 1 ObjectID 9 Maker CDWA 102 2 DublinCore 6 Creator CDWA 102 3 VRA 6 Creator CDWA 102 4 VRA v3.0 16 Creator CDWA 102 4 VRA v3.0 19 Personal Name CDWA 102 5 CIMI 68 Creator Name CDWA 102 5 CIMI 79 Creator General CDWA 102 6 USMARC 10 Main Entry CDWA 102 6 USMARC 11 Added Entry CDWA 102 15 Dalton Museum 6 Artist Name CDWA 102 Standards Field Mapping Mapping collection fields to standard fields to allow searching across separate collection databases • Maps Artist Nameto CDWA FieldID 102 (Creation-Creator-Identity-Names) Field Mapping Results for “Artist Name” Cornell Institute for Digital Collections

  14. “Built-in” Metadata Standards • Dublin Core • MARC • VRA 2.0 • VRA 3.0 • CDWA You can add whatever you like Cornell Institute for Digital Collections

  15. Implementation: Data into Insight • Currently, export desired data as Text files, clean it up, and import into Insight • This year – link tables between the two systems? Cornell Institute for Digital Collections

  16. What is ahead for Insight? • Development of stand-alone cataloging tool (May?) • Further support for hierarchical objects • Books, letters • Links to LDAP and Kerberos authentication • GIS support Cornell Institute for Digital Collections

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