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ProvenanceAnnotation Session

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Jim Myers, PNNL. Sanjeev Khanna, U. Penn. Peter Fox, UCAR ... Facilitated Discussion Peter Fox. Dimensions (1): Personal/group public/published scope ... – PowerPoint PPT presentation

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Title: ProvenanceAnnotation Session


1
Provenance/Annotation Session
Workshop on Data Derivation and Provenance
Chicago, October 17-18, 2002
  • Jim Myers, PNNL
  • Sanjeev Khanna, U. Penn.
  • Peter Fox, UCAR

2
Session Outline
  • Overview/Issues Jim Myers
  • Examples and Categorization of Provenance/Annotati
    on types Dave Pearson, Oracle UK
  • Complexity of Tracking P/A
  • Wang-Chiew Tan, UCSC
  • Facilitated Discussion Peter Fox

3
What is Provenance/Annotation?
  • Dimensions (1)
  • Personal/group ? public/published scope
  • Many ? Few
  • Informal ? Formal
  • high value to few ? low value to many
  • Conjecture ? Conclusion
  • Ask Dave (next talk)

4
What is Provenance/Annotation?
  • Dimensions(2)
  • Associated with files, parts of files, parts of
    multiple files, DB entries, DB Query Results,
  • Human use ? Machine Use
  • Aid discovery, an alternative to storage, a
    record/audit trail, an alternative to legacy
    papers

5
The P/A Lifecycle
Community
Personal/Group
6
Issues
  • Common Terminology
  • Types of P/A
  • Useful Categorizations
  • Uses of P/A

7
Issues 2
  • Variations in
  • Cost/Benefit
  • Who pays, who gains
  • Delay between costs and benefits

8
Issues 3
  • Write many, read one
  • Alternative encodings of P/A (directory
    structures, tacit knowledge, hardcoded
    assumptions,)
  • Schema Evolution

9
Questions
  • To what extent do P/A issues/strategies differ by
    category, community, use?
  • At what scale does coordination/standardization
    makes sense?
  • How much of lifecycle can be automated?
  • How can researchers participate in multiple P/A
    cycles
  • What uses have proven benefits?
  • What economic models have been shown to work?

10
Next talks
  • Dave Pearson Examples and categories of P/A
  • Wang-Chiew Tan Discussion of complexity of
    managing P/A data versus categories
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