Provenance and Annotation of Data

1. Front Matter

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2. Session 1: Keynotes

   1. Automatic Generation of Workflow Provenance

      • Roger S. Barga, Luciano A. Digiampietri

      Pages 1-9

   2. Managing Rapidly-Evolving Scientific Workflows

      • Juliana Freire, Cláudio T. Silva, Steven P. Callahan, Emanuele Santos, Carlos E. Scheidegger, Huy T. Vo

      Pages 10-18

3. Session 2: Applications

   1. Virtual Logbooks and Collaboration in Science and Software Development

      • Dimitri Bourilkov, Vaibhav Khandelwal, Archis Kulkarni, Sanket Totala

      Pages 19-27

   2. Applying Provenance in Distributed Organ Transplant Management

      • Sergio Álvarez, Javier Vázquez-Salceda, Tamás Kifor, László Z. Varga, Steven Willmott

      Pages 28-36

   3. Provenance Implementation in a Scientific Simulation Environment

      • Guy K. Kloss, Andreas Schreiber

      Pages 37-45

   4. Towards Low Overhead Provenance Tracking in Near Real-Time Stream Filtering

      • Nithya N. Vijayakumar, Beth Plale

      Pages 46-54

   5. Enabling Provenance on Large Scale e-Science Applications

      • Miguel Branco, Luc Moreau

      Pages 55-63

4. Session 4: Semantics 1

   1. Harvesting RDF Triples

      • Joe Futrelle

      Pages 64-72

   2. Mapping Physical Formats to Logical Models to Extract Data and Metadata: The Defuddle Parsing Engine

      • Tara D. Talbott, Karen L. Schuchardt, Eric G. Stephan, James D. Myers

      Pages 73-81

   3. Annotation and Provenance Tracking in Semantic Web Photo Libraries

      • Christian Halaschek-Wiener, Jennifer Golbeck, Andrew Schain, Michael Grove, Bijan Parsia, Jim Hendler

      Pages 82-89

   4. Metadata Catalogs with Semantic Representations

      • Yolanda Gil, Varun Ratnakar, Ewa Deelman

      Pages 90-100

   5. Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering

      • Jennifer Golbeck

      Pages 101-108

5. Session 5: Workflow

   1. Recording Actor State in Scientific Workflows

      • Ian Wootten, Omer Rana, Shrija Rajbhandari

      Pages 109-117

   2. Provenance Collection Support in the Kepler Scientific Workflow System

      • Ilkay Altintas, Oscar Barney, Efrat Jaeger-Frank

      Pages 118-132

   3. A Model for User-Oriented Data Provenance in Pipelined Scientific Workflows

      • Shawn Bowers, Timothy McPhillips, Bertram Ludäscher, Shirley Cohen, Susan B. Davidson

      Pages 133-147

   4. Applying the Virtual Data Provenance Model

      • Yong Zhao, Michael Wilde, Ian Foster

      Pages 148-161

6. Session 6: Models of Provenance, Annotations and Processes

   1. A Provenance Model for Manually Curated Data

      • Peter Buneman, Adriane Chapman, James Cheney, Stijn Vansummeren

      Pages 162-170

   2. Issues in Automatic Provenance Collection

      • Uri Braun, Simson Garfinkel, David A. Holland, Kiran-Kumar Muniswamy-Reddy, Margo I. Seltzer

      Pages 171-183

   3. Electronically Querying for the Provenance of Entities

      • Simon Miles

      Pages 184-192

Provenance is a well understood concept in the study of ?ne art, where it refers to the documented history of an art object. Given that documented history, the objectattains anauthority that allows scholarsto understandand appreciateits importance and context relative to other works. In the absence of such history, art objects may be treated with some skepticism by those who study and view them. Over the last few years, a number of teams have been applying this concept of provenance to data and information generated within computer systems. If the provenance of data produced by computer systems can be determined as it can for some works of art, then users will be able to understand (for example) how documents were assembled, how simulation results were determined, and how ?nancial analyses were carried out. A key driver for this research has been e-Science. Reproducibility of results and documentation of method have always been important concerns in science, and today scientists of many ?elds (such as bioinformatics, medical research, chemistry, and physics) see provenanceas a mechanism that can help repeat s- enti?cexperiments,verifyresults,andreproducedataproducts.Likewise,pro- nance o?ers opportunities for the business world, since it allows for the analysis of processes that led to results, for instance to check they are well-behaved or satisfy constraints; hence, provenance o?ers the means to check compliance of processes,on the basis of their actual execution. Indeed, increasing regulation of many industries (for example, ?nancial services) means that provenance reco- ing is becoming a legal requirement.

• Service Oriented Architecture (SOA)
• Simulation
• data annotation
• data derivation
• data provenance
• identity
• information documentation
• metadata management
• performance
• performance evaluation
• process documentation
• process provenance
• security
• trust
• virtual lofbooks

• School of Electronics and Computer Science, University of Southampton, Southhampton, UK

  Luc Moreau

• Mathematics & Computer Science Division, Argonne National Laboratory,  

  Ian Foster

This book constitutes the thoroughly referred postproceeding of the International Provenance and Annotation Workshops, IPAW 2006, held in Chicago, Il, USA in May 2006.

The 26 revised full papers presented together with 2 keynote papers were carefully selected for presentation during two rounds of reviewing and improvement. The paper are organized in topical sections on applications in software development, organ transplantation management, scientific simulation, stream filtering, and e-science; data semantics and semantic Web; workflows; models of provenance, annotations, and processes; and provenance systems.

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