Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Automated Process Discovery: A Literature Review and a Comparative Evaluation with Domain Experts

Äriprotsesside kaeve meetodi võimaldavad analüütikul kasutada logisid saamaks teadmisi protsessi tegeliku toimise kohta. Neist meetodist üks enim uuritud on automaatne äriprotsesside avastamine. Sündmuste logi võetakse kui sisend automaatse äriprotsesside avastamise meetodi poolt ning väljundina toodetakse äriprotsessi mudel, mis kujutab logis talletatud sündmuste kontrollvoogu. Viimase kahe kümnendi jooksul on väljapakutud mitmeidki automaatseid äriprotsessi avastamise meetodeid balansseerides erinevalt toodetavate mudelite skaleeruvuse, täpsuse ning keerukuse vahel. Siiani on automaatsed äriprotsesside avastamise meetodid testitud ad-hoc kombel, kus erinevad autorid kasutavad erinevaid andmestike, seadistusi, hindamismeetrikuid ning alustõdesid, mis viib tihti võrdlematute tulemusteni ning mõnikord ka mittetaastoodetavate tulemusteni suletud andmestike kasutamise tõttu. Eelpool toodu mõistes sooritatakse antud magistritöö raames süstemaatiline kirjanduse ülevaade automaatsete äriprotsesside avastamise meetoditest ja ka süstemaatiline hindav võrdlus üle nelja kvaliteedimeetriku olemasolevate automaatsete äriprotsesside avastamise meetodite kohta koostöös domeeniekspertidega ning kasutades reaalset logi rahvusvahelisest tarkvara firmast. Kirjanduse ülevaate ning hindamise tulemused tõstavad esile puudujääke ning seni uurimata kompromisse mudelite loomiseks nelja kvaliteedimeetriku kontekstis. Antud magistritöö tulemused võimaldavad teaduritel parandada puudujäägid meetodites. Samuti vastatakse küsimusele automaatsete äriprotsesside avastamise meetodite kasutamise kohta väljaspool akadeemilist maailma.Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual performance of these processes.One of the most widely studied process mining operations is automated process discovery.An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log.Several automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models.So far, automated process discovery methods have been evaluated in an ad hoc manner, with different authors employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of non-publicly available datasets.In this setting, this thesis provides a systematic review of automated process discovery methods and a systematic comparative evaluation of existing implementations of these methods with domain experts by using a real-life event log extracted from a international software engineering company and four quality metrics.The review and evaluation results highlight gaps and unexplored tradeoffs in the field in the context of four business process model quality metrics.The results of this master thesis allows researchers to improve the lacks in the automated process discovery methods and also answers question about the usability of process discovery techniques in industry

Business process performance measurement : a structured literature review of indicators, measures and metrics

Measuring the performance of business processes has become a central issue in both academia and business, since organizations are challenged to achieve effective and efficient results. Applying performance measurement models to this purpose ensures alignment with a business strategy, which implies that the choice of performance indicators is organization-dependent. Nonetheless, such measurement models generally suffer from a lack of guidance regarding the performance indicators that exist and how they can be concretized in practice. To fill this gap, we conducted a structured literature review to find patterns or trends in the research on business process performance measurement. The study also documents an extended list of 140 process-related performance indicators in a systematic manner by further categorizing them into 11 performance perspectives in order to gain a holistic view. Managers and scholars can consult the provided list to choose the indicators that are of interest to them, considering each perspective. The structured literature review concludes with avenues for further research

Guided Interaction Exploration and Performance Analysisin Artifact-Centric Process Models

Artifact-centric process models aim to describecomplex processes as a collection of interacting artifacts.Recent development in process mining allow for the dis-covery of such models. However, the focus is often on therepresentation of the individual artifacts rather than theirinteractions. Based on event data, composite state machi-nes representing artifact-centric processes can be discov-ered automatically. Moreover, the study provides ways ofvisualising and quantifying interactions among differentartifacts. For example, strongly correlated behaviours indifferent artifacts can be highlighted. Interesting correla-tions can be subsequently analysed to identify potentialcauses of process performance issues. The study provides astrategy to explore the interactions and performance dif-ferences in this context. The approach has been fullyimplemented as a ProM plug-in; the CSM Miner providesan interactive artifact-centric process discovery toolfocussing on interactions. The approach has been evaluatedusing real life data, to show that the guided exploration ofartifact interactions can successfully identify process per-formance issues

An Extension of Business Process Model and Notation for Security Risk Management

Kaasaegsed infosüsteemide arendamise metoodikad hõlmavad erinevaid tehnilisi äriprotsesside modelleerimise meetmeid. Äriprotsesside modelleerimiseks kasutatav keel (BPMN) on tänapäeval muutunud üheks standartseks meetmeks, mis edukalt rakendatakse infosüsteemide loomisel ning edasi arendamisel selleks, et ettevõtete äriprotsesse kirjeldada ja modelleerida.Vaatamata sellele, et BPMN on hea töörist, mille abil on võimalik ettevõtte äriprotsesse mõistma ja esitama, see ei võimalda äriprotsesside modelleerimisel adresseerida süsteemi turvalisuse aspekte. Autor leiab, et see on BPMN nõrk külg, selle pärast, et turvalise infosüsteemi arendamiseks on oluline nii äriprotsesse kui ka süsteemi turvalisust vaadeldada tervikuna. Käesolevas magistritöös autor töötab välja BPMN 2.0 keele jaoks uusi elemente, mis edaspidi peavad võimaldama adresseerima turvalisuse temaatika süsteemi modelleerimisel. Autori pakutud lahendus põhineb BPMN modelleerimiskeele seostamisel turvalisuse riski juhendamise metoodikaga (ISSRM). Antud magistritöös rakendatakse struktureeritud lähenemine BPMN peamiste aspektide analüüsimisel ja turvalisuse riskide juhtimiseks uute elementide väljatöötamisel, selleks ühildades BPMN ning ISSRM-i kontsepte. Magistritöös on demonstreeritud väljatöötatud lisaelementide kasutus, selgitatud kuidas antud elementidega laiendatud BPMN võimaldab väljendada ettevõtte varasid (assets), nendega seotuid riske (risks) ja riskide käsitlust (risk treatment). See on analüüsitud internetkaupluse varade konfidentsiaalsuse, terviklikkuse ja kättesaadavuse näitel. Autor on veendunud, et BPMN laienemine turvalisuse kontseptide osas ja antud töö raames tehtud konkreetsed ettepanekud aitavad infosüsteemide analüütikutele mõistma kuidas süsteemi turvalisust arendada nii, et läbi äriprotsessi tuvastatud olulisemate ettevõtte varade turvalisus oleks infosüsteemis käsitletud ning tagatud. Autori poolt antud käsitlus on vaadeldud ka laiemas mõttes, nimelt, BPMN keelele pakutud laienemisega avaneb perspektiiv äriprotsesside ja turvalisuse mudeleite koosvõimele ning BPMN-i teiste modelleerimise metoodikatega, nagu ISSRM või Secure Tropos, integreerimisele.Modern Information System (IS) development supports different techniques for business process modelling. Recently Business Process Model and Notation (BPMN) has become a standard that allows modelers to visualize organizational business processes. However, despite the fact that BPMN is a good approach to introduce and understand business processes, there is no opportunity to address security concerns while analysing the business needs. This is a problem, since both business processes and security concerns should be understood in parallel to support a development of the secure systems. In current thesis we introduce the extensions for BPMN 2.0 regarding security aspects. The following proposal is based on alignment of the modelling notation with IS security risk management (ISSRM).We apply a structured approach to understand major aspects of BPMN and propose extensions for security risk management based on the BPMN alignment to the ISSRM concepts. We demonstrate the use of extensions, illustrating how the extended BPMN could express assets, risks and risk treatment on few running examples related to the Internet store assets’ confidentiality, integrity and availability. We believe that our proposal would allow system analysts to understand how to develop security requirements to secure important assets defined through business processes. We also attempt to observe the following approach in the broader sense and we open a possibility for the business and security model interoperability and the model transformation between BPMN and another modelling approach also aligned to ISSRM, Secure Tropos

Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis

Within the process mining field, Deterministic Finite State Automata (DFAs) are largely employed as foundation mechanisms to perform formal reasoning tasks over the information contained in the event logs, such as conformance checking, compliance monitoring and cross-organization process analysis, just to name a few. To support the above use cases, in this paper, we investigate how to leverage Model Learning (ML) algorithms for the automated discovery of DFAs from event logs. DFAs can be used as a fundamental building block to support not only the development of process analysis techniques, but also the implementation of instruments to support other phases of the Business Process Management (BPM) lifecycle such as business process design and enactment. The quality of the discovered DFAs is assessed wrt customized definitions of fitness, precision, generalization, and a standard notion of DFA simplicity. Finally, we use these metrics to benchmark ML algorithms against real-life and synthetically generated datasets, with the aim of studying their performance and investigate their suitability to be used for the development of BPM tools