Abstract
This article presents an approach for automating business process consolidation by applying process topic clustering based on business process libraries, using graph mining algorithm to extract process patterns, find out frequent sub-graphs under the same process topic, then filling sub-graph information into the table of process frequent sub-graph, finally merging these frequent sub-graphs to get merged business processes on the basis of process merge algorithm. We use compression ratio to judging the capability of our merge methods, the compression ratios of integrated processes in same topic cluster are much lower than the different topic processes, and our method achieves similar compression ratio compare with previous work.
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Acknowledgment
This work is supported by the National Basic Research Program of China undergrant No. 2014CB340404, the National Natural Science Foundation of China under grant Nos. 61373037, 61562073, the Natural Science Foundation of Jiangxi Province the grant No. 20142BAB217028, the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning under Grant 2014R1A1A1005915.
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Huang, Y., You, I. (2016). Business Process Merging Based on Topic Cluster and Process Structure Matching. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_45
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DOI: https://doi.org/10.1007/978-981-10-0356-1_45
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