Case notion discovery and recommendation: automated event log building on databases

Process mining techniques use event logs as input. When analyzing complex databases, these event logs can be built in many ways. Events need to be grouped into traces corresponding to a case. Different groupings provide different views on the data. Building event logs is usually a time-consuming, ma...

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Bibliographic Details
Published in:Knowledge and information systems 2020-07, Vol.62 (7), p.2539-2575
Main Authors: de Murillas, E. González López, Reijers, H. A., van der Aalst, W. M. P.
Format: Article
Language:English
Subjects:
Algorithms
Automation
Computer Science
Customer relationship management
Data Mining and Knowledge Discovery
Database Management
Information retrieval
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Machine learning
Quality assessment
Regression analysis
Regular Paper
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Summary:Process mining techniques use event logs as input. When analyzing complex databases, these event logs can be built in many ways. Events need to be grouped into traces corresponding to a case. Different groupings provide different views on the data. Building event logs is usually a time-consuming, manual task. This paper provides a precise view on the case notion on databases, which enables the automatic computation of event logs. Also, it provides a way to assess event log quality, used to rank event logs with respect to their interestingness. The computational cost of building an event log can be avoided by predicting the interestingness of a case notion, before the corresponding event log is computed. This makes it possible to give recommendations to users, so they can focus on the analysis of the most promising process views. Finally, the accuracy of the predictions and the quality of the rankings generated by our unsupervised technique are evaluated in comparison to the existing regression techniques as well as to state-of-the-art learning to rank algorithms from the information retrieval field. The results show that our prediction technique succeeds at discovering interesting event logs and provides valuable recommendations to users about the perspectives on which to focus the efforts during the analysis.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-019-01430-6