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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Using Event Log Timing Information to Assist Process Scenario Discoveries
Event logs contain abundant information, such as activity
names, time stamps, activity executors, etc. However, much of existing
trace clustering research has been focused on applying activity names
to assist process scenarios discovery. In addition, many existing trace
clustering algorithms commonly used in the literature, such as k-means
clustering approach, require prior knowledge about the number of
process scenarios existed in the log, which sometimes are not known
aprior. This paper presents a two-phase approach that obtains timing
information from event logs and uses the information to assist process
scenario discoveries without requiring any prior knowledge about process
scenarios. We use five real-life event logs to compare the performance of
the proposed two-phase approach for process scenario discoveries with
the commonly used k-means clustering approach in terms of model’s
harmonic mean of the weighted average fitness and precision, i.e., the
F1 score. The experiment data shows that (1) the process scenario
models obtained with the additional timing information have both higher
fitness and precision scores than the models obtained without the timing
information; (2) the two-phase approach not only removes the need for
prior information related to k, but also results in a comparable F1 score
compared to the optimal k-means approach with the optimal k obtained
through exhaustive search.
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- NSF-PAR ID:
- 10311280
- Date Published:
- Journal Name:
- 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)
- 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/AIKE48582.2020.00017
