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Computers in Industry
Volume 53, Issue 3, April 2004, Pages 321-343
Process / Workflow Mining
 
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doi:10.1016/j.compind.2003.10.007    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2003 Elsevier B.V. All rights reserved.

Business Process Intelligence

Daniela GrigoriCorresponding Author Contact Information, E-mail The Corresponding Author, a, Fabio CasatiE-mail The Corresponding Author, b, Malu CastellanosE-mail The Corresponding Author, b, Umeshwar DayalE-mail The Corresponding Author, b, Mehmet SayalE-mail The Corresponding Author, b and Ming-Chien ShanE-mail The Corresponding Author, b

a Laboratoire PRiSM, CNRS FRE-2510, Université de Versailles St-Quentin en Yvelines, 45 avenue des États-Unis., 78035, Versailles Cedex, France b Hewlett-Packard, 1501 Page Mill Road, MS 1142, Palo Alto, CA 94304, USA

Available online 17 December 2003.

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Abstract

Business Process Management Systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. BPMSs have the ability of logging information about the business processes they support. Proper analysis of BPMS execution logs can yield important knowledge and help organizations improve the quality of their business processes and services to their business partners. This paper presents a set of integrated tools that supports business and IT users in managing process execution quality by providing several features, such as analysis, prediction, monitoring, control, and optimization. We refer to this set of tools as the Business Process Intelligence (BPI) tool suite. Experimental results presented in this paper are very encouraging. We plan to investigate further enhancements on the BPI tools suite, including automated exception prevention, and refinement of process data preparation stage, as well as integrating other data mining techniques.

Author Keywords: Business Process Intelligence; Workflow mining; Process execution analysis and prediction; Data warehouse

Article Outline

1. Introduction and motivations
2. Process model and process logs
3. BPI architecture and functionalities
3.1. The process data warehouse, data loader, and core semantic concepts
3.1.1. The PDW and the data loader
3.1.2. Semantic process analysis
3.2. The BPI process mining engine
3.2.1. Process mining phases for behavior analysis
3.2.2. Experimental results in behavior analysis
3.2.3. Prediction of behaviors
3.2.4. Analysis of decision points
3.2.5. Process mining engine architecture
3.3. The BPI Cockpit
4. Related work
4.1. History management
4.2. Workflow mining
4.3. Workflow monitoring and controlling
5. Conclusions and future work
References
Vitae











Computers in Industry
Volume 53, Issue 3, April 2004, Pages 321-343
Process / Workflow Mining
 
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