Process mining is a technique for extracting process models
from event logs. Event logs contain abundant information related to an
event such as the timestamp of the event, the actions that triggers the
event, etc. Much of existing process mining research has been focused
on discoveries of process models behind event logs. How to uncover the
timing constraints from event logs that are associated with the discovered
process models is not well-studied. In this paper, we present an approach
that extends existing process mining techniques to not only mine but
also integrate timing constraints with process models discovered and
constructed by existing process mining algorithms. The approach contains
three major steps, i.e., first, for a given process model constructed by
an existing process mining algorithm and represented as a workflow net,
extract a time dependent set for each transition in the workflow net model.
Second, based on the time dependent sets, develop an algorithm to extract
timing constraints from event logs for each transition in the model. Third,
extend the original workflow net into a time Petri net where the discovered
timing constraints are associated with their corresponding transitions. A
real-life road traffic fine management process scenario is used as a case
study to show how timing constraints in the fine management process
can be discovered from event logs with our approach.
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Improving Process Discovery Results by Filtering Out Outliers from Event Logs with Hidden Markov Models
Process Mining is a technique for extracting process models
from event logs. Event logs contain abundant explicit information related
to events, such as the timestamp and the actions that trigger the
event. Much of the existing process mining research has focused on
discovering the process models behind these event logs. However, Process
Mining relies on the assumption that these event logs contain accurate
representations of an ideal set of processes. These ideal sets of processes
imply that the information contained within the log represents what
is really happening in a given environment. However, many of these
event logs might contain noisy, infrequent, missing, or false process
information that is generally classified as outliers. Extending beyond
process discovery, there are many research efforts towards cleaning the
event logs to deal with these outliers. In this paper, we present an
approach that uses hidden Markov models to filter out outliers from event
logs prior to applying any process discovery algorithms. Our proposed
filtering approach can detect outlier behavior, and consequently, help
process discovery algorithms return models that better reflect the real
processes within an organization. Furthermore, we show that this filtering
method outperforms two commonly used filtering approaches, namely the
Matrix Filter approach and the Anomaly Free Automation approach for
both artificial event logs and real-life event logs.
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- NSF-PAR ID:
- 10311278
- Date Published:
- Journal Name:
- 2021 IEEE 23rd Conference on Business Informatics (CBI)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CBI52690.2021.00028
