collection building interfaces with luna insight
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Collection Building Interfaces with Luna Insight. Gale Halpern ( [email protected] ) Representing the Luna Development Team Mira Basara, Rick Silterra, Surinder Ghangas. Growing Image Collections. Large dynamic image collections managed in Luna Insight - PowerPoint PPT PresentationTRANSCRIPT
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Collection Building Interfaces with Luna InsightGale Halpern ([email protected])
Representing the Luna Development Team
Mira Basara, Rick Silterra, Surinder Ghangas
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Growing Image CollectionsLarge 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 Collection4. New York Aerial Photographs
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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.
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Number of Digital Images
(October 2007)
Anticipated Total number of Images
Current Image Rate of growth
Herbert F. Johnson Art Museum collection
21,339 36,000 + 100 per month
Knight Visual Resources
Collection16, 359 unlimited 600 per month
Collection Sizes
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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
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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?
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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.
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KVRF/Luna interface
PicTor Access Database
Knight Visual Resources Facility Server
Scanned Images (.tiffs)
Library 24 Server
Luna InsightOracle Database
TEXT FILESWorks, Images, Creators, Work Relationships
DCAPS
PC with Luna Media Batch ToolsImage
Derivatives(.jpegs)
CreateDerivatives
Uploaded TEXT FILES
Data Clean-up(PERL scripts)
CD’s containing .tiffs
Luna Indexer
Luna data upload
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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)
CreateDerivatives
Oracle views of TMS data
CD’s containing.tiffs
Luna IndexerPhoto Studio Server (Johnson Art Museum)
Luna InsightOracle Database
Oracle DB Link
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Knight Visual Resource CollectionPicTor
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Text FilesWorks.txt
Images.txt
Knight Visual Resource Collection
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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
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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
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Knight Visual Resource Collection
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Knight Visual Resource Collection
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The Museum System (TMS)
Herbert F. Johnson Museum Collection
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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
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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
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Herbert F. Johnson Museum Collection
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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.
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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.