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

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

doi:10.14778/1687553.1687606

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 journal › Article › peer-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 language	English (US)
Pages (from-to)	1614-1617
Number of pages	4
Journal	Proceedings of the VLDB Endowment
Volume	2
Issue number	2
DOIs	https://doi.org/10.14778/1687553.1687606
State	Published - Aug 2009
Externally published	Yes

All Science Journal Classification (ASJC) codes

Computer Science (miscellaneous)
General Computer Science

Access to Document

10.14778/1687553.1687606

Cite this

@article{ba25d1b9f01b4e3fb78abe148947772c,

title = "WOLVES: Achieving correct provenance analysis by detecting and resolving unsound workflow views",

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.",

author = "Peng Sun and Ziyang Liu and Sivaramakrishnan Natarajan and Davidson, {Susan B.} and Yi Chen",

year = "2009",

month = aug,

doi = "10.14778/1687553.1687606",

language = "English (US)",

volume = "2",

pages = "1614--1617",

journal = "Proceedings of the VLDB Endowment",

issn = "2150-8097",

publisher = "Very Large Data Base Endowment Inc.",

number = "2",

}

TY - JOUR

T1 - WOLVES

T2 - Achieving correct provenance analysis by detecting and resolving unsound workflow views

AU - Sun, Peng

AU - Liu, Ziyang

AU - Natarajan, Sivaramakrishnan

AU - Davidson, Susan B.

AU - Chen, Yi

PY - 2009/8

Y1 - 2009/8

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=79953250821&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953250821&partnerID=8YFLogxK

U2 - 10.14778/1687553.1687606

DO - 10.14778/1687553.1687606

M3 - Article

AN - SCOPUS:79953250821

SN - 2150-8097

VL - 2

SP - 1614

EP - 1617

JO - Proceedings of the VLDB Endowment

JF - Proceedings of the VLDB Endowment

IS - 2

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this