Scalable Online Conformance Checking Using Incremental Prefix-Alignment Computation

TL;DR

This paper presents a scalable, distributed method for exact online conformance checking of ongoing process executions, improving real-time deviation detection accuracy and practical applicability.

Contribution

It introduces a scalable, distributed implementation of an exact online conformance checking approach with two extensions to enhance efficiency and usability.

Findings

Effective in real process data sets

Reduces computational effort significantly

Improves accuracy of online deviation detection

Abstract

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.