Collection Building Interfaces with Luna InsightGale Halpern (geh12@cornell.edu)

Representing the Luna Development Team

Mira Basara, Rick Silterra, Surinder Ghangas

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

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.

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

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

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?

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.

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

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

Knight Visual Resource CollectionPicTor

Text FilesWorks.txt

Images.txt

Knight Visual Resource Collection

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

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

Knight Visual Resource Collection

Knight Visual Resource Collection

The Museum System (TMS)

Herbert F. Johnson Museum Collection

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

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

Herbert F. Johnson Museum Collection

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.

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.