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A Behavioral Similarity Metric for Semantic Workflows Based on Semantic Task Adjacency Relations With Importance
Author(s) -
Jinyong Sun,
Tianlong Gu,
Junyan Qian
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2731378
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
For modern enterprises and organizations, new business workflow can be constructed by reusing already available similar workflows in the repository. Workflow reuse is an important method for implementing business workflow management. Semantic workflows contain control-flow, data-flow, and semantic information relevant to a domain, which facilitates workflow reuse and adaptation. A similarity metric for semantic workflows is important for achieving workflow reuse. However, the existing similarity metrics for semantic workflows focus on workflow structures while ignoring their behaviours, which affect the quality of retrieved similar semantic workflows. Therefore, this paper proposes a behavioral similarity metric for semantic workflows based on semantic task adjacency relations with importance (ISTARs) that incorporate domain knowledge. First, ISTARs that involve semantic tasks and importance of semantic task adjacency relations are defined, and the ISTARs set is used to express the behaviour of the semantic workflow. The ISTAR similarity proposal is based on the similarity between two ISTARs sets and represents the similarity between semantic workflows. The ISTAR distance deduced from ISTAR similarity satisfies the properties of distance metrics. An experimental evaluation revealed that the proposed ISTAR similarity resulted in more effective retrieval of similar semantic workflows than did existing popular behavioral similarity measures.

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