WOLVES: Achieving correct provenance analysis by detecting and resolving unsound workflow views

Peng Sun, Ziyang Liu, Sivaramakrishnan Natarajan, Susan B. Davidson, Yi Chen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Workflow views abstract groups of tasks in a workflow into composite tasks, and are used for simplifying provenance analysis, workflow sharing and reuse. An unsound view does not preserve the dataflow between tasks in the workflow, and can therefore cause incorrect provenance analysis. In this demo we present WOLVES, a system that efficiently identifies and corrects unsound workflow views with minimal changes (view correction). Since the view correction problem is NP-hard, WOLVES allows the user to choose between two forms of local optimality, strong and weak. Efficient time algorithms achieving these optimalities are implemented in WOLVES.

Original languageEnglish (US)
Pages (from-to)1614-1617
Number of pages4
JournalProceedings of the VLDB Endowment
Volume2
Issue number2
DOIs
StatePublished - Aug 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

Fingerprint Dive into the research topics of 'WOLVES: Achieving correct provenance analysis by detecting and resolving unsound workflow views'. Together they form a unique fingerprint.

Cite this