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BPM
2009
Springer

Understanding Spaghetti Models with Sequence Clustering for ProM

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Understanding Spaghetti Models with Sequence Clustering for ProM
The goal of process mining is to discover process models from event logs. However, for processes that are not well structured and have a lot of diverse behavior, existing process mining techniques generate highly complex models that are often difficult to understand; these are called spaghetti models. One way to try to understand these models is to divide the log into clusters in order to analyze reduced sets of cases. However, the amount of noise and ad-hoc behavior present in real-world logs still poses a problem, as this type of behavior interferes with the clustering and complicates the models of the generated clusters, affecting the discovery of patterns. In this paper we present an approach that aims at overcoming these difficulties by extracting only the useful data and presenting it in an understandable manner. The solution has been implemented in ProM and is divided in two stages: preprocessing and sequence clustering. We illustrate the approach in a case study where it becom...
Gabriel M. Veiga, Diogo R. Ferreira
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where BPM
Authors Gabriel M. Veiga, Diogo R. Ferreira
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