Metadata and Cross-Collection Searching in Luna’s Insight
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Transcript of Metadata and Cross-Collection Searching in Luna’s Insight
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Cornell Institute for Digital Collections
Metadata and Cross-Collection Searching in Luna’s Insight
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Cornell Institute for Digital Collections
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
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Cornell Institute for Digital Collections
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
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Cornell Institute for Digital Collections
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
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Cornell Institute for Digital Collections
Is VRA 3.0 too complex?
• [example]
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Cornell Institute for Digital Collections
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
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Cornell Institute for Digital Collections
Insight Demo
• Selected features:– General search and display attributes– Cross-collection searching– Variable metadata displays– Annotation tool– Support for formats
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Cornell Institute for Digital Collections
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
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Cornell Institute for Digital Collections
Images
Intranet/Internet
ODBC
Image Server
Insight Application Server
HTML Server
HTML & Active Server
Pages
Active Server
Insight JVA Clients Insight Browser Clients
Database Server
Data
User Manager User Manager
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Values
Insight v3 Data Structure
Objects People Location Location Hierarchy Events
Source Data Tables
FieldGroups
Mapping Tables
Tables Joins Fields
Terms
Inverted Index Tables
• Replicates source data in a format common to all Insight collection databases
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Insight Virtual Collection Manager
CollectionManager
RepositoryB
RepositoryC
RepositoryA
VirtualCollection
A
VirtualCollection
B
VirtualCollection
C
VirtualCollection
D
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Cornell Institute for Digital Collections
Standards Field Mapping Mapping collection fields to standard fields to allow
searching across separate collection databases
• Maps Artist Name to CDWA FieldID 102 (Creation-Creator-Identity-Names)
Field Mapping Results for “Artist Name”
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
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Cornell Institute for Digital Collections
“Built-in” Metadata Standards
• Dublin Core
• MARC
• VRA 2.0
• VRA 3.0
• CDWA
You can add whatever you like
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Cornell Institute for Digital Collections
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?
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Cornell Institute for Digital Collections
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