3.8 Proceedings Paper

Creating Translucent Event Logs to Improve Process Discovery

Journal

PROCESS MINING WORKSHOPS, ICPM 2022
Volume 468, Issue -, Pages 435-447

Publisher

SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-031-27815-0_32

Keywords

Translucent event logs; Robotic process mining; Task mining; Desktop activity mining

Ask authors/readers for more resources

Event logs capture information about executed activities, but not about enabled but not executed activities. Translucent event logs contain information on enabled activities. We propose two techniques to increase the availability of translucent event logs. The first technique records system states as snapshots and links them to events, adding information about enabled activities. The second technique uses a process model to add information about enabled activities to existing event logs, improving process discovery.
Event logs capture information about executed activities. However, they do not capture information about activities that could have been performed, i.e., activities that were enabled during a process. Event logs containing information on enabled activities are called translucent event logs. Although it is possible to extract translucent event logs from a running information system, such logs are rarely stored. To increase the availability of translucent event logs, we propose two techniques. The first technique records the system's states as snapshots. These snapshots are stored and linked to events. A user labels patterns that describe parts of the system's state. By matching patterns with snapshots, we can add information about enabled activities. We apply our technique in a small setting to demonstrate its applicability. The second technique uses a process model to add information concerning enabled activities to an existing traditional event log. Data containing enabled activities are valuable for process discovery. Using the information on enabled activities, we can discover more correct models.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available