Gero J. Kolhof

Gero Kolhof, Sebastiaan J. van Zelst

Process models are, like event data, first-class citizens in most process mining approaches. Several process modeling formalisms have been proposed and used, e.g., Petri nets, BPMN, and process trees. Despite their frequent use, little research addresses the formal properties of process trees and the corresponding potential to improve the efficiency of solving common computational problems. Therefore, in this paper, we propose an invertible state space definition for process trees and demonstrate that the corresponding state space graph is isomorphic to the state space graph of the tree's inverse. Our result supports the development of novel, time-efficient, decomposition strategies for applications of process trees. Our experiments confirm that our state space definition allows for the adoption of bidirectional state space search, which significantly improves the overall performance of state space searches.

Daniel Schuster, Gero J. Kolhof

Conformance checking techniques aim to collate observed process behavior with normative/modeled process models. The majority of existing approaches focuses on completed process executions, i.e., offline conformance checking. Recently, novel approaches have been designed to monitor ongoing processes, i.e., online conformance checking. Such techniques detect deviations of an ongoing process execution from a normative process model at the moment they occur. Thereby, countermeasures can be taken immediately to prevent a process deviation from causing further, undesired consequences. Most online approaches only allow to detect approximations of deviations. This causes the problem of falsely detected deviations, i.e., detected deviations that are actually no deviations. We have, therefore, recently introduced a novel approach to compute exact conformance checking results in an online environment. In this paper, we focus on the practical application and present a scalable, distributed implementation of the proposed online conformance checking approach. Moreover, we present two extensions to said approach to reduce its computational effort and its practical applicability. We evaluate our implementation using data sets capturing the execution of real processes.