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Semantic similarity of workflow traces with various granularities
Citation
Liu, Q and Bai, Q and Yang, Y, Semantic similarity of workflow traces with various granularities, Proceedings of the 17th Web Information Systems Engineering International Conference (WISE 2016), Lecture Notes in Computer Science, volume 10041, 8-10 November 2016, Shanghai, China, pp. 211-226. ISBN 9783319487397 (2016) [Refereed Conference Paper]
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Copyright Statement
Copyright 2016 Springer
DOI: doi:10.1007/978-3-319-48740-3_15
Abstract
A workflow trace describes provenance information of a particular workflow execution. Understanding workflow traces and their similarity have many applications in both scientific research and business world. Given workflow traces generated by heterogeneous systems with difference granularities, it is a challenge for users to understand their similarities. In this work, we investigate workflow traces' granularity problem and their similarity method. Algorithms are developed to transform a trace into its multi-granularity forms assisting by a workflow trace ontology. A novel generic semantic similarity algorithm is proposed that not only considers the structural similarity but also the semantics coverage embedded in traces during transformation. Furthermore,theoretical analysis is presented to compute the maximum semantic similarity. Our approach enables that two workflow traces can be compared with any granularity. The experiment using real world workflow traces demonstrates the effectiveness of the proposed methods.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | proveance, workflow trace, granularity, workflow trace similarity |
Research Division: | Information and Computing Sciences |
Research Group: | Computer vision and multimedia computation |
Research Field: | Pattern recognition |
Objective Division: | Information and Communication Services |
Objective Group: | Information systems, technologies and services |
Objective Field: | Application software packages |
UTAS Author: | Bai, Q (Dr Quan Bai) |
ID Code: | 140709 |
Year Published: | 2016 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2020-09-02 |
Last Modified: | 2022-09-06 |
Downloads: | 22 View Download Statistics |
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