Quotation Di Ciccio, Claudio, Mecella, Massimo, Mendling, Jan. 2015. The Effect of Noise on Mined Declarative Constraints. In Data-Driven Process Discovery and Analysis, Hrsg. Paolo Ceravolo, Rafael Accorsi, Philippe Cudre-Mauroux, S. 1-24. Riva del Garda, Italy: Springer.




Declarative models are increasingly utilized as representational format in process mining. Models created from automatic process discovery are meant to summarize complex behaviors in a compact way. Therefore, declarative models do not define all permissible behavior directly, but instead define constraints that must be met by each trace of the business process. While declarative models provide compactness, it is up until now not clear how robust or sensitive different constraints are with respect to noise. In this paper, we investigate this question from two angles. First, we establish a constraint hierarchy based on formal relationships between the different types of Declare constraints. Second, we conduct a sensitivity analysis to investigate the effect of noise on different types of declarative rules. Our analysis reveals that an increasing degree of noise reduces support of many constraints. However, this effect is moderate on most of the constraint types, which supports the suitability of Declare for mining event logs with noise.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Contribution to conference proceedings
Language English
Title The Effect of Noise on Mined Declarative Constraints
Title of whole publication Data-Driven Process Discovery and Analysis
Editor Paolo Ceravolo, Rafael Accorsi, Philippe Cudre-Mauroux
Page from 1
Page to 24
Location Riva del Garda, Italy
Publisher Springer
Year 2015
ISBN 978-3-662-46435-9
URL http://dx.doi.org/10.1007/978-3-662-46436-6_1


European Wide Service Platform for Green European Transportation
Di Ciccio, Claudio (Former researcher)
Mendling, Jan (Details)
Mecella, Massimo (Sapienza, University of Rome, Italy)
Institute for Data, Process and Knowledge Management (AE Sabou) (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1105 Computer software (Details)
1109 Information and data processing (Details)
5306 Business data processing (Details)
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