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

A Petri net view of covalent bonds

In nature and chemistry the interactions among elements often form bonds and among them covalent bonds are relevant, involving the sharing of electrons. Another relevant and compelling facet of calculi modelling covalent bonds is that certain steps in reactions are the result of concerting different activities, possibly reversing some of them. Starting from a calculus for covalent bonds, we investigate on how it can be done in a compositional fashion and how it can be encoded in suitable Petri nets. The outcome gives us a compositional covalent bond calculus and a truly distributed implementation. On these results it is possible to build a behavioural equivalence among terms.Fil: Melgratti, Hernan Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Mezzina, Claudio Antares. Università Degli Studi Di Urbino Carlo Bo; ItaliaFil: Pinna, G. Michele. Università degli Studi di Cagliari; Itali

A new operational representation of dependencies in Event Structures

The execution of an event in a complex and distributed system where the dependencies vary during the evolution of the system can be represented in many ways, and one of them is to use Context-Dependent Event structures. Event structures are related to Petri nets. The aim of this paper is to propose what can be the appropriate kind of Petri net corresponding to Context-Dependent Event structures, giving an operational flavour to the dependencies represented in a Context/Dependent Event structure. Dependencies are often operationally represented, in Petri nets, by tokens produced by activities and consumed by others. Here we shift the perspective using contextual arcs to characterize what has happened so far and in this way to describe the dependencies among the various activities

Täpne ja tõhus protsessimudelite automaatne koostamine sündmuslogidest

Töötajate igapäevatöö koosneb tegevustest, mille eesmärgiks on teenuste pakkumine või toodete valmistamine. Selliste tegevuste terviklikku jada nimetatakse protsessiks. Protsessi kvaliteet ja efektiivsus mõjutab otseselt kliendi kogemust – tema arvamust ja hinnangut teenusele või tootele. Kliendi kogemus on eduka ettevõtte arendamise oluline tegur, mis paneb ettevõtteid järjest rohkem pöörama tähelepanu oma protsesside kirjeldamisele, analüüsimisele ja parendamisele. Protsesside kirjeldamisel kasutatakse tavaliselt visuaalseid vahendeid, sellisel kujul koostatud kirjeldust nimetatakse protsessimudeliks. Kuna mudeli koostaja ei suuda panna kirja kõike erandeid, mis võivad reaalses protsessis esineda, siis ei ole need mudelid paljudel juhtudel terviklikud. Samuti on probleemiks suur töömaht - inimese ajakulu protsessimudeli koostamisel on suur. Protsessimudelite automaatne koostamine (protsessituvastus) võimaldab genereerida protsessimudeli toetudes tegevustega seotud andmetele. Protsessituvastus aitab meil vähendada protsessimudeli loomisele kuluvat aega ja samuti on tulemusena tekkiv mudel (võrreldes käsitsi tehtud mudeliga) kvaliteetsem. Protsessituvastuse tulemusel loodud mudeli kvaliteet sõltub nii algandmete kvaliteedist kui ka protsessituvastuse algoritmist. Antud doktoritöös anname ülevaate erinevatest protsessituvastuse algoritmidest. Toome välja puudused ja pakume välja uue algoritmi Split Miner. Võrreldes olemasolevate algoritmidega on Splint Miner kiirem ja annab tulemuseks kvaliteetsema protsessimudeli. Samuti pakume välja uue lähenemise automaatselt koostatud protsessimudeli korrektsuse hindamiseks, mis on võrreldes olemasolevate meetoditega usaldusväärsem. Doktoritöö näitab, kuidas kasutada optimiseerimise algoritme protsessimudeli korrektsuse suurendamiseks.Everyday, companies’ employees perform activities with the goal of providing services (or products) to their customers. A sequence of such activities is known as business process. The quality and the efficiency of a business process directly influence the customer experience. In a competitive business environment, achieving a great customer experience is fundamental to be a successful company. For this reason, companies are interested in identifying their business processes to analyse and improve them. To analyse and improve a business process, it is generally useful to first write it down in the form of a graphical representation, namely a business process model. Drawing such process models manually is time-consuming because of the time it takes to collect detailed information about the execution of the process. Also, manually drawn process models are often incomplete because it is difficult to uncover every possible execution path in the process via manual data collection. Automated process discovery allows business analysts to exploit process' execution data to automatically discover process models. Discovering high-quality process models is extremely important to reduce the time spent enhancing them and to avoid mistakes during process analysis. The quality of an automatically discovered process model depends on both the input data and the automated process discovery application that is used. In this thesis, we provide an overview of the available algorithms to perform automated process discovery. We identify deficiencies in existing algorithms, and we propose a new algorithm, called Split Miner, which is faster and consistently discovers more accurate process models than existing algorithms. We also propose a new approach to measure the accuracy of automatically discovered process models in a fine-grained manner, and we use this new measurement approach to optimize the accuracy of automatically discovered process models.https://www.ester.ee/record=b530061

TraVaG: Differentially Private Trace Variant Generation Using GANs

Process mining is rapidly growing in the industry. Consequently, privacy concerns regarding sensitive and private information included in event data, used by process mining algorithms, are becoming increasingly relevant. State-of-the-art research mainly focuses on providing privacy guarantees, e.g., differential privacy, for trace variants that are used by the main process mining techniques, e.g., process discovery. However, privacy preservation techniques for releasing trace variants still do not fulfill all the requirements of industry-scale usage. Moreover, providing privacy guarantees when there exists a high rate of infrequent trace variants is still a challenge. In this paper, we introduce TraVaG as a new approach for releasing differentially private trace variants based on \text{Generative Adversarial Networks} (GANs) that provides industry-scale benefits and enhances the level of privacy guarantees when there exists a high ratio of infrequent variants. Moreover, TraVaG overcomes shortcomings of conventional privacy preservation techniques such as bounding the length of variants and introducing fake variants. Experimental results on real-life event data show that our approach outperforms state-of-the-art techniques in terms of privacy guarantees, plain data utility preservation, and result utility preservation