Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review

Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from prevalent information systems like CRM and ERP, these techniques aim to tackle the deficiency, convergence, and divergence issues seen in traditional event logs. Despite the promise, the adoption in real-world process mining analyses remains limited. This paper embarks on a comprehensive literature review of object-centric process mining, providing insights into the current status of the discipline and its historical trajectory

Generalized alignment-based trace clustering of process behavior

Process mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a subnet of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets.Peer ReviewedPostprint (author's final draft

Improving explicit model checking for Petri nets

Model checking is the automated verification that systematically checks if a given behavioral property holds for a given model of a system. We use Petri nets and temporal logic as formalisms to describe a system and its behavior in a mathematically precise and unambiguous manner. The contributions of this thesis are concerned with the improvement of model checking efficiency both in theory and in practice. We present two new reduction techniques and several supplementary strength reduction techniques. The thesis also enhances partial order reduction for certain temporal logic classes

Prototype Selection using Clustering and Conformance Metrics for Process Discovery

International audienceAutomated process discovery algorithms aim to automatically create process models based on event data that is captured during the execution of business processes. These algorithms usually tend to use all of the event data to discover a process model. Using all (i.e., less common) behavior may lead to discover imprecise and/or complex process models that may conceal important information of processes. In this paper, we introduce a new incremental prototype selection algorithm based on the clustering of process instances to address this problem. The method iteratively computes a unique process model from a different set of selected prototypes that are representative of whole event data and stops when conformance metrics decrease. This method has been implemented using both ProM and RapidProM. We applied the proposed method on several real event datasets with state-of-the-art process discovery algorithms. Results show that using the proposed method leads to improve the general quality of discovered process models