Conformance Checking-based Concept Drift Detection in Process Mining

One of the main challenges of process mining is to obtain models that represent a process as simply and accurately as possible. Both characteristics can be greatly influenced by changes in the control flow of the process throughout its life cycle. In this thesis we propose the use of conformance metrics to monitor such changes in a way that allows the division of the log into sub-logs representing different versions of the process over time. The validity of the hypothesis has been formally demonstrated, showing that all kinds of changes in the process flow can be captured using these approaches, including sudden, gradual drifts on both clean and noisy environments, where differentiating between anomalous executions and real changes can be tricky

Gradual Drift Detection in Process Models Using Conformance Metrics

Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the state-of-the-art focus on the detection of sudden changes, leaving aside other types of changes. In this paper, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay and change region overlapping than the main state-of-the-art algorithms

CONDA-PM -- A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.Comment: 45 pages, 11 tables, 13 figure

Process Mining in Industry

Protsessikaeve on kogum analüüstehnikaid, mis võimaldab saada andmetepõhist ülevaadet äriprotsesside tegelikust toimimisest. Protsessikaeve kasutamiseks tuleb äriprotsessi täitmise andmed salvestada spetsiaalselt protsessikaeve jaoks loodud andmetüüpi – sündmuslogisse. Sündmuslogi on äriprotsessi kronoloogiline tegevuste järjekord. Antud uurimisvaldkond on üsnagi noor ning seda pole veel ettevõtetes laialdaselt kasutusele võetud. Siiski, protsessikaeve kohta ilmub üha rohkem teadustöid. Antud töös tehakse süstemaatiline kirjanduse ülevaade, mis selgitab välja erinevad protsessikaeve tehnikad, mida on testitud kasutades päris elulisi sündmuslogisid. Koostatud kirjanduse ülevaadet kasutati küsitluse läbi viimiseks ettevõtete esindajate seas, et aru saada kui kasulikuks iga protsessikaeve tehnikat peetakse. Küsitluse sihtgrupp olid Eesti ettevõtete esindajad kelle tulemusi võrreldi ettevõtete esindajate üle maailma.Process mining is a set of analysis techniques that provides a databased overview of how business processes are actually executed. In order to use process mining techniques the data about the business process execution has to be recorded into a chronological sequence of activities called event logs. It is a quite young research area and has not yet been widely adapted by the industry. However,more and more research is being produced in the field. In this paper a systematic literature review was conducted to identify all the different process mining techniques that have been tested on reallife logs. This review was used as an input to a survey among industry representatives to understand how useful each process mining technique is considered from the perspective of the industry. The target group of the survey were industry representatives in Estonia, who were compared with industry representatives from around the world