مقالات ISI مدیریت

Controlled automated discovery of collections of business process models

A B S T R A C T

Automated process discovery techniques aim at extracting process models from information
system logs. Existing techniques in this space are effective when applied to relatively
small or regular logs, but generate spaghetti-like and sometimes inaccurate models when
confronted to logs with high variability. In previous work, trace clustering has been
applied in an attempt to reduce the size and complexity of automatically discovered
process models. The idea is to split the log into clusters and to discover one model per
cluster. This leads to a collection of process models – each one representing a variant of
the business process – as opposed to an all-encompassing model. Still, models produced
in this way may exhibit unacceptably high complexity and low fitness. In this setting, this
paper presents a two-way divide-and-conquer process discovery technique, wherein the
discovered process models are split on the one hand by variants and on the other hand
hierarchically using subprocess extraction. Splitting is performed in a controlled manner
in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life
logs show that the technique produces collections of models substantially smaller than
those extracted by applying existing trace clustering techniques, while allowing the user
to control the fitness of the resulting models.
& 2014 Elsevier Ltd. All rights reserved.

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کد محصول : شماره 34

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