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

Analysis of Business Process Management Software

Antud bakalaureuse töö eesmärgiks on analüüsida olemasolevat äriprotsesside juhtimise tarkvara ning leida võimalused funktsionaalsuse ja efektiivsuse arendamiseks ning tuua välja ka kitsaskohad ja kasutamisvõimalused koolis. Töö sisuks on Signavio, Bizagi, draw.io ning ProM kirjeldused ning nende kasutamisel tehtud tähelepanekud. Iga peatüki lõpus on ka järelduste osa. Saadud tulemustest on näha, et kõiki uuritud programme on võimalik kasutada edukalt ülikooli tasemel. Samuti selgus, et programmides ei esinenud kriitilise tähtsusega puudujääke. Autor järeldas, et antud tööd on võimalik edasi kasutada baasdokumendina äriprotsesside juhtimise tarkvarapaketi või ProMi dokumentatsiooni loomiseks.The goal of the given bachelor thesis is to analyse existing business process management software and to find possibilities to add functionality and increase effieceny and also to point out shortcomings and possibilities of using analysed software at school. The content of given thesis is descriptions of Signavio, Bizagi, draw.io and ProM and also made notes during their usage. At the end of every chapter is a section for conclusions. The obtained results show that it is possible to use all reviewed programs successfully at university level. Results also showed that the programs did not have any shortcomings or flaws with critical importancy. The author concluded that it is possible to use given thesis in the future as a basis document for developing a business process management suit or as a basis document for creating documentation for ProM

Automated discovery of process models from event logs: Review and benchmark

Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark covering twelve publicly-available real-life event logs and eight quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of several methods and a strong divergence in their performance with respect to the different quality metrics used

Data underlying the paper: Automated Discovery of Process Models from Event Logs: Review and Benchmark

This dataset comprises the public event logs used in the study "Automated Discovery of Process Modelsfrom Event Logs: Review and Benchmark" by Adriano Augusto, Raffaele Conforti, Marlon Dumas, Marcello La Rosa,Fabrizio Maria Maggi, Andrea Marrella, Massimo Mecella, and Allar Soo. The dataset contains 12 publicly available of the event logs of the IEEE Task Force on Process Mining - Event Logs, of which 7 have been pre-processed to remove infrequent behavior

Automated Discovery of Process Models: Results of Systematic Literature Search

The Excel workbooks in this dataset contain the references retrieved during a Systematic Literature Review (SLR) on automated process discovery from event logs.<br>There is one workbook per query: <br>- Process Learning<br>- Workflow Learning<br>- Process Discovery<br>- Workflow Discovery<br><br