Process mining using BPMN : relating event logs and process models

Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more involved BPMN models in comparison with the initial models are identified. Keywords: Process mining; Process discovery; Conformance checking; BPMN (Business Process Model and Notation); Petri nets; Bisimulatio

Discovering, analyzing and enhancing BPMN models using ProM

Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a process model based on an event log. Process mining is not limited to process discovery and also includes conformance checking and model enhancement. Conformance checking techniques are used to diagnose the deviations of the observed behavior as recorded in the event log from some process model. Model enhancement allows to extend process models using additional perspectives, conformance and performance information. In recent years, BPMN (Business Process Model and Notation) 2.0 has become a de facto standard for modeling business processes in industry. This paper presents the BPMN support current in ProM. ProM is the most known and used open-source process mining framework. ProM’s functionalities of discovering, analyzing and enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will help ProM users to bridge the gap between formal models (such as Petri nets, causal nets and others) and process models used by practitioners

Enhancing BPMN Conformance Checking with OR Gateways and Data Objects

Äriprotsessimudel ja -notatsioon (BPMN) on arenev standard äriprotsesside graafiliseks kujutamiseks. Protsessimudel kirjeldab, kuidas äriprotsess peaks toimima. Kui äriprotsessi tegelikust käitamisest on saadaval ka sündmuste logi, on võimalik vastata küsimusele, kas protsessimudel vastab tegelikkusele. Vastavusanalüüs püüab tuvastada mittevastavusi protsessimudeli ja äriprotsessi käitamisel tekkinud sündmuste logi vahel. BPMN vastavuseanalüsaator on üks Itaalia ettevõtte SIAV-i poolt arendatud protsessikaeve tööriista osadest. Nimetatud tööriistal on aga puudujäägid formaalse semantika osas. Nimelt keskendub vastavusanalüüs järgnevuse voole (control-flow) protsessis, kuid jätab arvesse võtmata andmetevahelisi sõltuvusi. Lisaks ei ole vastavusanalüüsil võimalik kasutada protsessimudeleid, mis sisaldavad OR väravaid (OR gateway). OR-join omab mitme-tähenduslikku semantikat. Se lle konstruktsiooni jaoks on pakutud mitmeid formaalseid semantikaid sarnastes keeltes, nagu EPCs ja YAWL. Nimetatud semantikate kasutatamine mudelite käitamisel ja vastavuse analaüüsil on aga arvutuslikult kulukas. Seega on käesolevas lõputöös implementeeritud OR värava aktiveerimine lineaarse ajalise sõltuvusega mudeli suuruse suhtes. Kuna SIAV-i vastavusanalüsaator ei võta arvesse andmetevahelisi sõltuvusi, võib puudulik analüüs viia vigase vastavusdiagnostikani. Näiteks võib andmeatribuut anda infot selle kohta, et käitati vale tegevus. Kirjeldatud põhjustel ei peaks vastavusanalüsaator tegelema vaid järgnevuse voo vastavuse analüüsiga, vaid peaks arvesse võtma ka andmeid ja nendevahelisi sõltuvusi ning aega. Käesoleva töö teises osas täiendati olemasolevat andmeanalüsaatorit andmeatribuutidega.The Business Process Model and Notation is a developing standard for capturing business processes. Process models describe how the business process is expected to be executed. When a log is available from process executions, this situation raises the interesting question “Are the model and the log conformant?". Conformance checking, also referred to as conformance analysis, aims at the detection of inconsistencies between a process model and its corresponding execution log.The BPMN conformance checker, as a part of a process mining tool, developed an Italian company called SIAV, however, this tool lacks some formal semantics. In particular, the previous conformance checking approach in SIAV tends to focus on the control-flow in a process, while abstracting from data dependencies and process models containing OR gateways could not be used.OR-join has an ambiguous semantics. The several formal semantics of this construct have been proposed for similar languages such as EPCs and YAWL. However, executing and verifying models using these semantics is computationally expensive. Therefore, in this thesis, we implemented enablement of an OR-join in linear time in the size of the workflow graph.Data dependencies are also not considered in conformance checker developed in SIAV, which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. That’s why the conformance checker should not only describe the process behaviour from the control flow point of view, but also from other perspectives like data or time. In the second part of the thesis, we enhanced the existing conformance checker with data attributes

Process Mining applied to BPMN-E2

Dissertação de mestrado em Engenharia InformáticaProcess Mining is characterized by a group of techniques that aim to mine and analyze event logs in an effort to extract patterns and useful insights regarding a business process, allowing for a better and more efficient understanding of it. This topic is sparking increasing interest in both academia and business contexts, which results in fast advances in the algorithms being applied, as well as in the subjacent notations used for process modeling. One of the most used notations for process modeling is Business Processs Model and Notation (BPMN), being its expressiveness in representing processes its strongest attribute. However, this notation reveals some flaws when dealing with some specific contexts, struggling to model activity duration, quality control and activity effects in context-specific resources. For this particular purpose, an extension named Business Processs Model and Notation Extended Expressiveness (BPMN-E2 ) was developed to tackle the limitations found on the original notation. In this dissertation, a new conformance checking algorithm was developed focusing on finding non-conformities between an event log and process models, taking into consideration the new elements that BPMN-E2 has to offer. Fuelled by a few setbacks found during this work, an event log clustering technique was also developed to downsize large event logs without stripping its representativity. Furthermore, the BPMN-E2 notation was used to model a real-life process and the developed conformance checking algorithm was applied to illustrate its analytical potential.Process Mining caracteriza um conjunto de técnicas que permitem a mineração e análise de event logs com o principal objetivo de extrair destes padrões e informações relevantes que permitam uma melhor percepção e eficiência dos processos realizados num determinado contexto. Esta área tem verificado um interesse crescente, tanto em meio académico como em meio empresarial, sendo notados avanços quer nos algoritmos de mineração utilizados, quer nas notações subjacentes utilizadas para modelar processos. Uma das notações mais utilizadas por profissionais e académicos é o Business Processs Model and Notation (BPMN) devido à sua expressividade na representação de processos. No entanto, esta mesma notação apresenta alguns inconvenientes quando é usada em determinados contextos, sendo difícil representar, por exemplo, durações de atividades, controlo de qualidade e efeitos da atividade nas características de um produto. Num esforço para resolver estes problemas, foi desenvolvida uma extensão chamada Business Processs Model and Notation Extended Expressiveness (BPMN-E2 ). Neste projeto foi desenvolvido um novo algoritmo de conformance checking, tendo em consideração a informação complementar oferecida pelo BPMN-E2 . Motivada por alguns contratempos durante o trabalho, uma técnica de clustering foi também desenvolvida para reduzir o tamanho de event logs sem afetar a sua representatividade. A notação BPMN-E2 foi também usada para modelar um processo real e o algoritmo de conformance checking usado nesse contexto para ilustrar o seu potencial analítico

Data-aware Conformance Checking

Vastavuse kontrollimine on üks kõige tavalisemaid ülesandeid protsessikaeve valdkonnas. Vastavuse kontrollimise peamine eesmärk on kontrollida protsessimudeli vastavust sündmuste logidele selleks, et hinnata või kirjeldada kuidas registreeritud käitumine protsessimudelis kirjeldatud käitumisest erineb. Enamus olemasolevatest vastavuse kontrollimise tehnikaid põhineb kontrollvoolu perspektiivile. Käesolev lõputöö pakub välja tehnika, mis lisaks kontrollvoolule põhinevale tehnikale arvestab ka andmete perspektiivile. Väljapakutud lähenemisviis on implementeeritud tarkvaralise lahendusena, mis kasutab sisendiks BPMN mudelit ja sündmuste logi. Loodud tarkvara töörist on loodud kasutades programmeerimiskeelt Elixir. Lõputöö sisaldab samuti ka välja töötatud lahenduse tulemuslikkuse hinnangut.Conformance checking is one of the most common tasks in the field process mining. The goal of conformance checking is to compare a process model against an event log in order to quantify or describe how the behavior recorded in the log deviates with respect to the behavior captured by the process model. Most of the existing conformance checking techniques focus on the control-flow perspective. In this thesis, we propose a conformance checking technique that takes into account the data perspectives in addition to the control-flow perspective. The proposed approach is implemented as a tool that takes as input a BPMN process model and an event log. The tool has been implemented using the Elixir programming language. The thesis also reports on a performance evaluation of the proposed approach