Dealing with Complex Parallel Structures in Process Discovery

Üks protsessikaeve eesmärkidest on leida protsessimudeleid logifailidest. Samas sõltub leitava protsessimudeli kvaliteet sellest, kui täielik informatsioon protsessi käitumise kohta logifailis on, kuna paralleelarvutuste keerukuse kasv on faktoraalses sõltuvuses harude hulgast. Selles lõputöös tutvustatakse uut algoritmi, mis kombineerib jaga-ja-valitse võtet olemasolevate kaevealgoritmidega, et täiustada hästistruktureeritud ja samaaegselt toimuvate tegumitega protsessimudelite kaevet poolikutest logifailidest. See töö kirjeldab väljapakutud algoritmi ja selgitab, kuidas see töötab samm-sammu haaval illustratiivsete kaeveprotsessi näidete abil. Lõpuks hindame selle meetodi efektiivsust ja tulemuslikkust kasutades protsessimudeleid, mis sisaldavad samaaegselt toimuvaid tegumeid ja juhuslikult loodud mudeleid.One of the aims of process mining is to discover a process model from a log. However, the quality of the discovered model depends on the completeness of the information about the process behaviour contained in the log. Incomplete logs do not provide all the possible behaviours. Existing process discovery algorithms dealing with incomplete logs, have troubles when working with complex parallel structures, because parallel behaviour has factorial rate of growth with respect to the number of branches. In this work, a new algorithm is proposed, which combines divide and conquer approach, with the existing mining algorithms to improve discovery of highly structured and highly concurrent process models from incomplete logs. This work describes the proposed algorithm, and explains how it works with illustrative step-by-step examples of the mining procedure. Finally, we evaluate the effectiveness and efficiency of our approach by using process models containing complex parallel structures and randomly generated models

Weakly Complete Event Logs in Process Mining

Many information systems have a possibility to record their execution, and, in this way, to generate a trace about events describing the real system behaviour. From behaviour example records in traces of the event log, the α-algorithm automatically generates a process model that belongs to a subclass of Petri nets, known as workflow nets. One of the basic limiting assumptions of α-algorithm is that the event log needs to be complete. As a result of attempting to overcome the problem of completeness of the event log, we introduced the notion of weakly complete event logs, from which our modified technique and algorithm can produce the same result as the α-algorithm from complete logs on parallel processes. Thereby weakly complete logs can be significantly smaller than complete logs, considering the number of traces they consist of. Weakly complete logs were used for the realization of our idea of interactive parallel business process model generation

Discovering learning processes using inductive miner: A case study with learning management systems (LMSs)

Resumen tomado de la publicaciónDescubriendo procesos de aprendizaje aplicando Inductive Miner: un estudio de caso en Learning Management Systems (LMSs). Antecedentes: en la minería de procesos con datos educativos se utilizan diferentes algoritmos para descubrir modelos, sobremanera el Alpha Miner, el Heuristic Miner y el Evolutionary Tree Miner. En este trabajo proponemos la implementación de un nuevo algoritmo en datos educativos, el denominado Inductive Miner. Método: hemos utilizado datos de interacción de 101 estudiantes universitarios en una asignatura de grado desarrollada en la plataforma Moodle 2.0. Una vez prepocesados se ha realizado la minería de procesos sobre 21.629 eventos para descubrir los modelos que generan los diferentes algoritmos y comparar sus medidas de ajuste, precisión, simplicidad y generalización. Resultados: en las pruebas realizadas en nuestro conjunto de datos el algoritmo Inductive Miner es el que obtiene mejores resultados, especialmente para el valor de ajuste, criterio de mayor relevancia en lo que respecta al descubrimiento de modelos. Además, cuando ponderamos con pesos las diferentes métricas seguimos obteniendo la mejor medida general con el Inductive Miner. Conclusiones: la implementación de Inductive Miner en datos educativos es una nueva aplicación que, además de obtener mejores resultados que otros algoritmos con nuestro conjunto de datos, proporciona modelos válidos e interpretables en términos educativos.Universidad de Oviedo. Biblioteca de Psicología; Plaza Feijoo, s/n.; 33003 Oviedo; Tel. +34985104146; Fax +34985104126; [email protected]

Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure