Understanding Collaborative Studies through Interoperable Workflow Provenance

  • Ilkay Altintas
  • Manish Kumar Anand
  • Daniel Crawl
  • Shawn Bowers
  • Adam Belloum
  • Paolo Missier
  • Bertram Ludäscher
  • Carole A. Goble
  • Peter M. A. Sloot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6378)


The provenance of a data product contains information about how the product was derived, and is crucial for enabling scientists to easily understand, reproduce, and verify scientific results. Currently, most provenance models are designed to capture the provenance related to a single run, and mostly executed by a single user. However, a scientific discovery is often the result of methodical execution of many scientific workflows with many datasets produced at different times by one or more users. Further, to promote and facilitate exchange of information between multiple workflow systems supporting provenance, the Open Provenance Model (OPM) has been proposed by the scientific workflow community. In this paper, we describe a new query model that captures implicit user collaborations. We show how this model maps to OPM and helps to answer collaborative queries, e.g., identifying combined workflows and contributions of users collaborating on a project based on the records of previous workflow executions. We also adopt and extend the high-level Query Language for Provenance (QLP) with additional constructs, and show how these extensions allow non-expert users to express collaborative provenance queries against this model easily and concisely. Furthermore, we adopt the Provenance Challenge 3 (PC3) workflows as a collaborative and interoperable usecase scenario, where different stages of the workflow are executed in three different workflow environments - Kepler, Taverna, and WSVLAM. Through this usecase, we demonstrate how we can establish and understand collaborative studies through interoperable workflow provenance.


Dependency Graph Lineage Graph Path Query Lineage Edge Provenance Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ilkay Altintas
    • 1
    • 2
  • Manish Kumar Anand
    • 1
  • Daniel Crawl
    • 1
  • Shawn Bowers
    • 3
  • Adam Belloum
    • 2
  • Paolo Missier
    • 4
  • Bertram Ludäscher
    • 5
  • Carole A. Goble
    • 4
  • Peter M. A. Sloot
    • 2
  1. 1.San Diego Supercomputer CenterUniversity of CaliforniaSan DiegoUSA
  2. 2.Computational ScienceUniversity of AmsterdamThe Netherlands
  3. 3.Department of Computer ScienceGonzaga UniversityUSA
  4. 4.School of Computer ScienceUniversity of ManchesterManchesterUK
  5. 5.UC Davis Genome CenterUniversity of CaliforniaDavisUSA

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