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Conference Papers Year : 2015

The Effect of Noise on Mined Declarative Constraints

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Abstract

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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Dates and versions

hal-01746406 , version 1 (29-03-2018)

Licence

Attribution - CC BY 4.0

Identifiers

  • HAL Id : hal-01746406 , version 1

Cite

Claudio Di Ciccio, Massimo Mecella, Jan Mendling. The Effect of Noise on Mined Declarative Constraints. 3rd International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Aug 2013, Riva del Garda, Italy. pp.1-24. ⟨hal-01746406⟩
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