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Author Guangmeng Zhai ♦ Tun Lu ♦ Xing Huang ♦ Zhaocan Chen ♦ Xianghua Ding ♦ Ning Gu
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2012
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword proteomics data analysis ♦ scientific workflow ♦ provenance ♦ workflow matching and discovery
Abstract Provenance plays a fundamental role in e-science to keep track of the data processing execution, evaluate the data quality, reproduce the analysis results, and especially share and re-use workflows. How to take full advantage of provenance to help scientists discover, match and select scientific workflows is a challenging work. Although some studies have been done to model, store, and query scientific workflows, little is done to build practical systems to support workflow matching and discovery. In this paper, we devise and implement a Provenance-Based Workflow Matching and Discovery System (PWMDS) for task-based pipelines in a proteomics data analysis platform called CoPExplorer to address the above challenge. With the proposed novel provenance model and workflow matching & discovery algorithms, PWMDS can provide scientists a ranked list of suitable service candidates for their specified workflows, and initial experiments demonstrate its effectiveness.
Description Author affiliation: School of Computer Science, Fudan University, Shanghai, China (Guangmeng Zhai; Tun Lu; Xing Huang; Zhaocan Chen; Xianghua Ding; Ning Gu)
ISBN 9781467312110
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2012-05-23
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781467312127
Size (in Bytes) 1.22 MB
Page Count 8
Starting Page 456
Ending Page 463

Source: IEEE Xplore Digital Library