Probabilistic workflow mining

Citation data:

Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05, Page: 275-284

Publication Year:
2005
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Citations 11
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Repository URL:
https://works.bepress.com/jijizhang/29, http://commons.ln.edu.hk/sw_master/4385
DOI:
10.1145/1081870.1081903
Author(s):
SILVA, Ricardo, ZHANG, Jiji, SHANSHAN, James G.
Publisher(s):
Association for Computing Machinery (ACM), ACM, ACM Press
conference paper description
In several organizations, it has become increasingly popu- lar to document and log the steps that makeup a typical business process. In some situations, a normative workflow model of such processes is developed, and it becomes im- portant to know if such a model is actually being followed by analyzing the available activity logs. In other scenarios, no model is available and, with the purpose of evaluating cases or creating new production policies, one is interested in learning a workflow representation of such activities. In either case, machine learning tools that can mine workflow models are of great interest and still relatively unexplored. We present here a probabilistic workflow model and a corre- sponding learning algorithm that runs in polynomial time. We illustrate the algorithm on example data derived from a real world workflow.

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