Unsupervised event abstraction using pattern abstraction and local process models

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs

Unsupervised event abstraction using pattern abstraction and local process models

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs

Unsupervised event abstraction using pattern abstraction and local process models

Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of abstraction, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and, then, use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs

Conformidade em processos de negócio baseada em classificação de documentos e mineração de log de eventos

The growing necessity for quality involves information systems and business processes. As a consequence, there is a growing demand to align the mapped and understood process of organizations with the performance of how the process is actually performed on a daily basis. With automated systems, the technology industry currently records events in information systems and generates data with ease, which are produced to generate value and insights to improve performance in the most diverse areas of organizations. In this work, we propose a study on conformance checking for generated models from the event log of an information system. A study that considers the main algorithms and metrics used in the literature to measure conformance checking in process mining, how the algorithms are defined and in which tools the approaches are tested. The proposed approach evaluates the semi-structured event log; for thus, text classification techniques are used to prepare the required structure of the event log. The main objective is to evaluate the conformance checking applied to the process mining area in order to analyze the extracted log, contextualizing the value approach with the definition of the existing process mapped from the manager understanding. To support this use in other data sets, the proposed model intends to be extensive for modification and use in other scenarios.A progressiva demanda pela qualidade subsidia os sistemas de informação e processos de negócios. Consequentemente, há uma crescente carência para o alinhamento do processo entendido e mapeado nas organizações, além de uma escassez no entendimento da execução do processo no dia a dia. Com sistemas automatizados, a indústria de tecnologia registra eventos e gera dados com facilidade, dados esses que podem produzir valor e insights, visando melhoria de desempenho em diversas áreas organizacionais. Pensando nisso, neste trabalho propõe-se um estudo sobre a avaliação da conformidade nos modelos gerados a partir do log de eventos de um sistema de informação. Um estudo que contemple os principais algoritmos e métricas utilizados na literatura para mensurar a conformidade em mineração de processos, como algoritmos são definidos e em quais ferramentas as abordagens são testadas. A abordagem proposta avalia o log de eventos semiestruturados; para isso, técnicas de classificação de texto são utilizadas na preparação da estrutura requerida do log de eventos. O objetivo é avaliar a abordagem da conformidade aplicada à área de mineração de processos para analisar o log extraído, contextualizando o valor da abordagem com a definição do processo existente mapeado a partir da visão do gestor. Para apoiar o uso em outros conjuntos de dados, o modelo proposto pretende ser extensivo para modificação e uso em outros cenários