Workflow mining with InWoLvE Joachim Herbst a,* , Dimitris Karagiannis b a DaimlerChrysler AG, Postfach 2360, 89013 Ulm, Germany b University of Vienna, Vienna, Austria Abstract State of the art information systems are based on explicit process models called workflow models. Experience from industrial practice shows that the definition of workflow models is a very time consuming and error prone task. Recently, there has been an increasing interest in applying techniques from data mining and machine learning to support this task. This approach has also been termed as process or workflow mining. In this paper, we give an overview of the algorithms that were implemented within the InWoLvE workflow mining system, we summarize the most important results of their experimental evaluation and we present the experiences that were made in the first industrial application of InWoLvE. # 2003 Elsevier B.V. All rights reserved. Keywords: Workflow mining; Machine learning; Workflow management 1. Introduction State of the art information systems are based on explicit process models called workflow models. These models are interpreted by one or more workflow engines to drive the execution of business processes within or across several enterprises. Experience from industrial practice shows that the definition of work- flow models is a very time consuming and error prone task. In depth knowledge of the business process and the ability to represent this knowledge using a formal workflow modelling language are needed for this task. Recently, there has been an increasing interest in applying techniques from data mining and machine learning to support this task [1–9]. This approach has also been termed as process or workflow mining. The basic idea of the workflow mining approach is to collect traces of workflow executions and to derive a workflow model from these observations. This is useful for example if some information system sup- porting the process, that logs all relevant events, is already in place before the workflow model is defined. Furthermore workflow mining techniques and advanced workflow technology, which is moving towards more operational flexibility [10–13], enable an evolutionary approach to the development of work- flow applications, where an initially roughly defined and informal or semi-formal workflow model is itera- tively refined and formalized. In this paper, we give an overview of the algorithms that were implemented within the InWoLvE work- flow mining system, we summarize the most impor- tant results of their experimental evaluation and we present the experiences that were made in the first industrial application of InWoLvE. The remainder of this paper is organized as follows. Section 2 defines the most important terms used throughout this paper. Section 3 formalizes the workflow mining problem, it defines problem classes and it gives an overview of the Computers in Industry 53 (2004) 245–264 * Corresponding author. E-mail address: joachim.j.herbst@daimlerchrysler.com (J. Herbst). 0166-3615/$ – see front matter # 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.compind.2003.10.002