Abstract
Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability. However, the bulk of approaches for automated discovery of declarative process models focus on the control-flow perspective, ignoring the data perspective. This paper addresses the problem of discovering declarative process models with data conditions. Specifically, the paper tackles the problem of discovering constraints that involve two activities of the process such that each of these two activities is associated with a condition that must hold when the activity occurs. The paper presents and compares two approaches to the problem of discovering such conditions. The first approach uses clustering techniques in conjunction with a rule mining technique, while the second approach relies on redescription mining techniques. The two approaches (and their variants) are empirically compared using a combination of synthetic and real-life event logs. The experimental results show that the former approach outperforms the latter when it comes to re-discovering constraints artificially injected in a log. Also, the former approach is in most of the cases more computationally efficient. On the other hand, redescription mining discovers rules with higher confidence (and lower support) suggesting that it may be used to discover constraints that hold for smaller subsets of cases of a process.
Highlights
Automated process discovery techniques take as input an event log recording the execution of instances of a business process over a period of time, and produce as output a process model that captures the behavior observed in the log
This paper focuses on the problem of discovering declarative process models from event logs
We presented two approaches to enhance Declare constraints with data conditions that relate the occurrence of pairs of events in a case of an event log
Summary
Automated process discovery techniques take as input an event log recording the execution of instances of a business process over a period of time, and produce as output a process model that captures the behavior observed in the log. Maggi et al / Information Systems 89 (2020) 101482 and a condition on the target (on the type of assessment) is satisfied The former condition is called an activation condition, the latter is called a target condition, and when both conditions are present in a Declare constraint, we say that the constraint contains two correlated data conditions. The paper proposes two approaches for automated discovery of declarative process models with correlated data conditions. Both techniques start by discovering a set of frequent constraints from an event log. The conference version focused on the first type of approach (clustering followed by rule discovery).
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