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Learning Workflow Models from Event Logs Using Co-clustering

Learning Workflow Models from Event Logs Using Co-clustering

Xumin Liu, Chen Ding
Copyright: © 2013 |Volume: 10 |Issue: 3 |Pages: 18
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781466634176|DOI: 10.4018/ijwsr.2013070103
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MLA

Liu, Xumin, and Chen Ding. "Learning Workflow Models from Event Logs Using Co-clustering." IJWSR vol.10, no.3 2013: pp.42-59. http://doi.org/10.4018/ijwsr.2013070103

APA

Liu, X. & Ding, C. (2013). Learning Workflow Models from Event Logs Using Co-clustering. International Journal of Web Services Research (IJWSR), 10(3), 42-59. http://doi.org/10.4018/ijwsr.2013070103

Chicago

Liu, Xumin, and Chen Ding. "Learning Workflow Models from Event Logs Using Co-clustering," International Journal of Web Services Research (IJWSR) 10, no.3: 42-59. http://doi.org/10.4018/ijwsr.2013070103

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Abstract

The authors propose a co-clustering approach to extract workflow models by analyzing event logs. The authors consider two major issues that are overlooked by most of the existing process mining approaches. First, a complex system typically runs multiple workflow models, all of which share the same log system. However, current approaches mainly focus on learning a single workflow model from event logs. Second, most systems support multi-users and each user is typically associated with (or use) certain number of operation sequences, which may follow one or more than one workflow models. Users can thus be leveraged as an important context when learning workflow models. However, this is not considered by current approaches. Therefore, the authors propose to learn User Behavior Pattern (UBP) that reflects the usage pattern of a user when accessing a business process system and exploit it to discover multiple workflow models from the event log of a complex system. The authors model a UBP as a probabilistic distribution on sequences, which allows computing the similarity between UBPs and sequences. The authors then co-cluster users and sequences to generate two types of clusters: user clusters that group users sharing similar UBP, and sequence clusters that group sequences that are the instances of the same workflow models. The workflow model can then be learned by analyzing its instances. The authors conducted a comprehensive experimental study to evaluate the effectiveness and efficiency of the proposed approach.

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