Defining Cases and Variants for Object-Centric Event Data

International Conference on Process Mining (ICPM), pp. 128–135

Abstract

The execution of processes leaves traces of event data in information systems. These event data can be analyzed through process mining techniques. For traditional process mining techniques, one has to associate each event with exactly one object, e.g., the company’s customer. Events related to one object form an event sequence called a case. A case describes an end-to-end run through a process. The cases contained in event data can be used to discover a process model, detect frequent bottlenecks, or learn predictive models. However, events encountered in real-life information systems, e.g., ERP systems, can often be associated with multiple objects. The traditional sequential case concept falls short of these so-called object-centric event data since these data exhibit a graph structure. One might force object-centric event data into the traditional case concept by flattening it. However, flattening manipulates the data and removes information. Therefore, a concept analogous to the case concept of traditional event logs is necessary to enable the application of different process mining tasks on object-centric event data. In this paper, we introduce the case concept for object-centric process mining: process executions. These are graph-based generalizations of cases as considered in traditional process mining. Furthermore, we provide techniques to extract process executions. Based on these executions, we determine equivalent process behavior with respect to an attribute using graph isomorphism. Equivalent process executions with respect to the event’s activity are object-centric variants, i.e., a generalization of variants in traditional process mining. We provide a visualization technique for object-centric variants. The contribution’s scalability and efficiency are extensively evaluated. Furthermore, we provide a case study showing the most frequent object-centric variants of a real-life event log. Our contributions might be used as a basis to adapt traditional process mining techniques by researchers and to generate initial control-flow insights into object-centric event logs by practitioners.

Authors 5

1. Jan Niklas Adams Aachen Chair for Process and Data Science (PADS)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair for Process and Data Science (PADS),Aachen,Germany

   Chair for Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany

2. Daniel Schuster

   Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Institute for Applied Information Technology (FIT),Fraunhofer,Sankt Augustin,Germany

   Institute for Applied Information Technology (FIT), Fraunhofer, Sankt Augustin, Germany

3. Seth Schmitz Aachen Laboratory for Machine Tools (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools (WZL),Aachen,Germany

   Laboratory for Machine Tools (WZL), RWTH Aachen University, Aachen, Germany

4. Günther Schuh Aachen Laboratory for Machine Tools (WZL)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Laboratory for Machine Tools (WZL),Aachen,Germany

   Laboratory for Machine Tools (WZL), RWTH Aachen University, Aachen, Germany

5. Wil M. P. van der Aalst Aachen Chair for Process and Data Science (PADS)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair for Process and Data Science (PADS),Aachen,Germany

   Chair for Process and Data Science (PADS), RWTH Aachen University, Aachen, Germany