Process Mining as a Strategy of Inquiry: Understanding Design Interventions and the Development of Business Processes

Process (re-)design and improvement are important aspectsof the Business Process Management (BPM) life-cycle. Yet, there is lit-tle empirical evidence on how design interventions materialize in actualprocess execution, leading to repeated failure of such initiatives. In thisdissertation I use the emerging affordances of process mining algorithmsto address this important limitation. In particular, I devise a methodthat combines process mining and grounded theory to study processualphenomena. Consequently, this method is applied to investigate changein business processes. This thesis contributes to the body of knowledgein BPM and bordering disciplines by demonstrating how process min-ing can be used as a method to study processual phenomena. Furtherthis research sheds light on the impact of design interventions on actualprocess execution and vica versa

Efficient Algorithms for Discovering Concept Drift in Business Processes.

Protsessikaeve on suhteliselt uus, kuid ühiskonna poolt juba kasutusele võetud uurimisvaldkond. Paljud ettevõtted ja asutused rakendavad erinevaid infosüsteemidega toetatud protsesse, mille käivitamisest jäävad maha sündmuste logid. Neid logisid analüüsides saab ehitada mudeli, mis kajastab, kuidas need protsessid reaalselt toimivad. Tänapäevased algoritmid eeldavad, et analüüsitav protsess on stabiilne, kuid tegelikult võib seda mõjutada hooaegsus, uus seadus või mõni väline sündmus – näiteks järsk majanduslangus. Sellisel juhul on tegemist kontseptsiooninihkega. Kontseptsiooninihked võivad olla järsud (kui protsessi muutus on äkiline) või järkjärgulised (kui üks protsessivariant asendub teisega sujuvalt). Antud töös pakkusime välja viis uudset lähenemist kontseptsiooninihke avastamiseks protsessikaeves. Igaüks neist parandab või laiendab algset Bose poolt kirjeldatud algoritmi [1]. Sammu pikkuse suurendamine võimaldab algoritmi kiirendada, jättes välja mõned vahepealsed sammud. Muutmispunkti automaatne leidmine võimaldab ekstraheerida kontseptsiooninihke punktid ilma manuaalse analüüsita. Adapteerivate akende algoritm (ADWIN) pehmendab originaalse algoritmi sõltuvust populatsiooni suurusest, seega vähendab vale-positiivsete ja vale-negatiivsete tulemuste arvu. Mittejärjestikkuste populatsioonidega algoritm võimaldab uurida järkjärgulisi kontseptsiooninihkeid. Lisaks lubab populatsioonide suuruste määramine ajaliste perioodide kaupa (jälgede koguse asemel) leida mikro-taseme ja makro-taseme nihked multi-taseme dünaamikaga logides, kus protsess muutub mitmel detailsuse tasemel. Kõik algoritmid olid implementeetirud ProM raamistiku Concept Drift moodulis. Algoritmide kvaliteedi hindamiseks pakub käesolev töö välja meetodi, kus CPN Tools programmi abil genereeritakse logisid erinevate kontseptsiooninihke tunnustega. Samuti on välja arendatud kvaliteedi hindamise raamistik, mis sarnaneb sellega, mis on kasutusel infootsingu valdkonnas ning mis hõlmab endas tegelike positiivsete, valepositiivsete ja valenegatiivsete väärtuste loendamist ning tuletatud meetrikate arvutamist. Algoritmid olid edukalt testitud nii simuleeritud, kui ka päriselu andmetega. [1] Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling Concept Drift in Process Mining. In: CAiSE. LNCS, vol. 6741, pp. 391–405.Springer, Berlin (2011)Process mining is a relatively new research area, but it is already used in practice. Every company and organization run different business processes, which are supported by information systems and which leave event logs while being executed. By analyzing those logs one can build a process model, which reflects how the process operates in reality.Existing algorithms assume that the analyzed process is in steady state, however it could be altered because of seasonality, a new law or some event, like a financial crisis. In this case, we have to deal with concept drift. Concept drifts can be sudden, when the change is abrupt and gradual, where one concept fades gradually while the other takes over. In this work we proposed five novel approaches for detecting concept drifts in process mining. All of them improve or expand the algorithm, proposed by Bose et al [1]. Step size improvement allows to speed up the algorithm by leaving out some intermediate steps. Automatic change point detection algorithm allows to extract the concept drift points without the need to analyze the plot manually. The adaptive windows algorithm (ADWIN) relaxes the original algorithm's dependency on the fixed population size, thus reducing the amount of false positives and false negatives. The algorithm with non-continuous populations allows to deal with gradual drifts. And finally, defining the population sizes in terms of time periods instead of trace amount allows to detect micro-level and macro-level drifts in logs with multi-order dynamics, where process changes can happen on multiple level of granularity. The algorithms were implemented in the Concept Drift plug-in of ProM framework. For assessing the quality of algorithms, we proposed a way to generate logs with different concept drift characteristics using CPN Tools and a quality evaluation framework, similar to the one used in the field information retrieval, involving calculating true positives, false positives, false negative and derived metrics. The algotihms were successfully tested on both simulated and real-life data. [1] Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling Concept Drift in Process Mining. In: CAiSE. LNCS, vol. 6741, pp. 391–405.Springer, Berlin (2011

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

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

Comparing Concept Drift Detection with Process Mining Software

Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use