Abstract
Information systems supporting business processes generate event data which provide the starting point for a range of process mining techniques.
Lion's share of real-life processes are complex and ad-hoc, which creates problems for traditional process mining techniques, that cannot deal with such unstructured processes.
Finding mainstream and deviating cases in such data is problematic, since most cases are unique and therefore determining what is normal or exceptional may depend on many factors.
Trace clustering aims to group similar cases in order to find variations of the process and to gain novel insights into the process at hand.
However, few trace clustering techniques take the context of the process into account and focus on the control-flow perspective only.
Outlier detection techniques provide only a binary distinction between normal and exceptional behavior, or depend on a normative process model to be present.
As a result, existing techniques are less suited for processes with a high degree of variability.
In this paper, we introduce a novel trace clustering technique that is able to find process variants as well as deviating behavior based on a set of selected perspectives.
Evaluation on both artificial and real-life event data reveals that additional insights can consequently be achieved.
Lion's share of real-life processes are complex and ad-hoc, which creates problems for traditional process mining techniques, that cannot deal with such unstructured processes.
Finding mainstream and deviating cases in such data is problematic, since most cases are unique and therefore determining what is normal or exceptional may depend on many factors.
Trace clustering aims to group similar cases in order to find variations of the process and to gain novel insights into the process at hand.
However, few trace clustering techniques take the context of the process into account and focus on the control-flow perspective only.
Outlier detection techniques provide only a binary distinction between normal and exceptional behavior, or depend on a normative process model to be present.
As a result, existing techniques are less suited for processes with a high degree of variability.
In this paper, we introduce a novel trace clustering technique that is able to find process variants as well as deviating behavior based on a set of selected perspectives.
Evaluation on both artificial and real-life event data reveals that additional insights can consequently be achieved.
| Original language | English |
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| Title of host publication | 27th Benelux Conference on Artificial Intelligence, 5-6 November 2015, Hasselt, Belgium |
| Number of pages | 8 |
| Publication status | Published - 5 Nov 2015 |
| Event | 27th Benelux Conference on Artificial Intelligence (BNAIC 2015) - Hasselt, Belgium Duration: 5 Nov 2015 → 6 Nov 2015 Conference number: 27 http://bnaic2015.org/ |
Conference
| Conference | 27th Benelux Conference on Artificial Intelligence (BNAIC 2015) |
|---|---|
| Abbreviated title | BNAIC 2015 |
| Country/Territory | Belgium |
| City | Hasselt |
| Period | 5/11/15 → 6/11/15 |
| Internet address |