Data Mining von Workflow-Protokollen zur teilautomatisierten Konstruktion von Prozeßmodellen

Maxeiner, Marco K.; Küspert, Klaus; Leymann, Frank

doi:10.1007/978-3-642-56687-5_7

Cited by 17 publications

(18 citation statements)

References 2 publications

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“…The idea of process mining is not new (van der van der Aalst and Weijters 2004;Agrawal et al 1998;Cook and Wolf 1998a,1998bHerbst 2000Herbst , 2001Herbst andKaragiannis 2000, 2004;Cook et al 2004;Maruster et al 2002;Maxeiner et al 2001;Schimm 2002Schimm , 2004Pinter and Golani 2004;Weijters and van der Aalst 2003 Model is complete and precise (1=yes, 0=no)…”

Section: Related Workmentioning

confidence: 99%

“…However, the resulting conformal graph is not a complete model. In Maxeiner et al (2001), a tool based on these algorithms is presented. Pinter and Golani (2004) extends the work in Agrawal et al (1998) to also consider the time information in the logs and, consequently, better detect concurrent behavior.…”

Section: Fig 31mentioning

confidence: 99%

“…A notable difference with other approaches is that the same activity can appear multiple times in the workflow model, i.e., the approach allows for duplicate activities. The graph generation technique is similar to the approach of Agrawal et al (1998) and Maxeiner et al (2001). The nature of splits and joins (i.e., AND or XOR) is discovered in the transformation step, where the stochastic activity graph is transformed into an ADONIS workflow model with block-structured splits and joins.…”

Section: Fig 31mentioning

confidence: 99%

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Genetic process mining: an experimental evaluation

Medeiros

Weijters

Aalst

2007

Data Min Knowl Disc

380

299

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One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework.

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Section: Related Workmentioning

confidence: 99%

Section: Fig 31mentioning

confidence: 99%

Section: Fig 31mentioning

confidence: 99%

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Genetic process mining: an experimental evaluation

Medeiros

Weijters

Aalst

2007

Data Min Knowl Disc

380

299

View full text Add to dashboard Cite

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“…The work described in this paper has been inspired by process mining from event logs [5,6,9,[15][16][17][18]24,25,[36][37][38][42][43][44]. The basic idea was to test the process mining techniques developed in workflow and case-handling environments in a more general enterprise information systems environment.…”

Section: Related Workmentioning

confidence: 99%

“…[5,6,9,[15][16][17]24,25,[36][37][38][42][43][44]. Process mining requires logs from enterprise information systems such as workflow management systems, case-handling systems and Enterprise Resource Planning Systems.…”

Section: Introductionmentioning

confidence: 99%

Mining configurable enterprise information systems

Jansen-Vullers

Aalst

Rosemann

2006

Data & Knowledge Engineering

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Process mining is the extraction of a process model from system logs. These logs have to meet minimum requirements, i.e. each event should refer to a case and a task. Many system logs do not meet these requirements, and therefore it is not possible to use process mining for process optimization or delta analysis. This paper shows an alternative process mining procedure for logs containing data on the frequency that process steps have been executed. To be able to mine such logs we apply Configurable Event-driven Process Chains (C-EPCs). If a C-EPC is available, we propose a method to mine the process. If only a classical reference model (i.e. an EPC) is available, we propose a method to first derive the C-EPC through mining and then analyse the process. This approach enables us to do process mining in the context of ERP systems such as SAP R/3.

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