Abstract
Declarative process management has emerged as an alternative solution for describing flexible workflows. In turn, the modelling opportunities with languages such as Declare are less intuitive and hard to implement. The area of process discovery covers the automatic discovery of process models. It has been shown that the performance of process mining algorithms, particularly when considering the multi-perspective declarative process models, are not satisfactory. State-of-the-art mining tools do not support multi-perspective declarative models at this moment. We address this open research problem by proposing an efficient mining framework that leverages the latest big data analysis technology and builds upon the distributed processing method MapReduce. The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints. Additionally, we provide a novel implementation concept allowing an easy introduction and discovery of customised constraint templates. We evaluated the mining performance and effectiveness of the presented approach on several real-life event logs. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable time.
Highlights
The research field of process mining refers to the automated discovery, conformance checking and enhancement of business process models
The paper at hand further completes the research on multi-perspective declarative process mining by extending our previous work in various ways; in particular, we introduce algorithms and descriptions for the full set of commonly accepted types of MP-Declare constraints
On top of a detailed analysis of most commonly used MP-Declare constraints with respect to an efficient discovery from process logs based on MapReduce, we provide a sophisticated framework which implements this process mining procedure
Summary
The research field of process mining refers to the automated discovery, conformance checking and enhancement of business process models. In [14,15], first approaches to enable the discovery of MP-Declare constraints based on SQL and relational databases have been proposed. It has not been investigated how this complex mining task can be performed in an efficient way. In our previous work [16], we first addressed this open research problem by proposing an efficient mining framework for discovering MP-Declare models that leverages latest big data analysis technology and builds upon the distributed processing method MapReduce. The results highlight that, with our efficient mining technique, multi-perspective declarative process models can be extracted in reasonable duration.
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