A study of the application of computational intelligence and machine learning techniques in business process mining

Mineração de processos é uma área de pesquisa relativamente recente que se situa entre mineração de dados e aprendizado de máquina, de um lado, e modelagem e análise de processos de negócio, de outro lado. Mineração de processos visa descobrir, monitorar e aprimorar processos de negócio reais por meio da extração de conhecimento a partir de logs de eventos disponíveis em sistemas de informação orientados a processos. O principal objetivo deste trabalho foi avaliar o contexto de aplicação de técnicas provenientes das áreas de inteligência computacional e de aprendizado de máquina, incluindo redes neurais artificiais. Para fins de simplificação, denominadas no restante deste texto apenas como ``redes neurais\'\'. e máquinas de vetores de suporte, no contexto de mineração de processos. Considerando que essas técnicas são, atualmente, as mais aplicadas em tarefas de mineração de dados, seria esperado que elas também estivessem sendo majoritariamente aplicadas em mineração de processos, o que não tinha sido demonstrado na literatura recente e foi confirmado por este trabalho. Buscou-se compreender o amplo cenário envolvido na área de mineração de processos, incluindo as principais caraterísticas que têm sido encontradas ao longo dos últimos dez anos em termos de: tipos de mineração de processos, tarefas de mineração de dados usadas, e técnicas usadas para resolver tais tarefas. O principal enfoque do trabalho foi identificar se as técnicas de inteligência computacional e de aprendizado de máquina realmente não estavam sendo amplamente usadas em mineração de processos, ao mesmo tempo que se buscou identificar os principais motivos para esse fenômeno. Isso foi realizado por meio de um estudo geral da área, que seguiu rigor científico e sistemático, seguido pela validação das lições aprendidas por meio de um exemplo de aplicação. Este estudo considera vários enfoques para delimitar a área: por um lado, as abordagens, técnicas, tarefas de mineração e ferramentas comumente mais usadas; e, por outro lado, veículos de publicação, universidades e pesquisadores interessados no desenvolvimento da área. Os resultados apresentam que 81% das publicações atuais seguem as abordagens tradicionais em mineração de dados. O tipo de mineração de processos com mais estudo é Descoberta 71% dos estudos primários. Os resultados deste trabalho são valiosos para profissionais e pesquisadores envolvidos no tema, e representam um grande aporte para a áreaMining process is a relatively new research area that lies between data mining and machine learning, on one hand, and business process modeling and analysis, on the other hand. Mining process aims at discovering, monitoring and improving business processes by extracting real knowledge from event logs available in process-oriented information systems. The main objective of this master\'s project was to assess the application of computational intelligence and machine learning techniques, including, for example, neural networks and support vector machines, in process mining. Since these techniques are currently widely applied in data mining tasks, it would be expected that they were also widely applied to the process mining context, which has been not evidenced in recent literature and confirmed by this work. We sought to understand the broad scenario involved in the process mining area, including the main features that have been found over the last ten years in terms of: types of process mining, data mining tasks used, and techniques applied to solving such tasks. The main focus of the study was to identify whether the computational intelligence and machine learning techniques were indeed not being widely used in process mining whereas we sought to identify the main reasons for this phenomenon. This was accomplished through a general study area, which followed scientific and systematic rigor, followed by validation of the lessons learned through an application example. This study considers various approaches to delimit the area: on the one hand, approaches, techniques, mining tasks and more commonly used tools; and, on the other hand, the publication vehicles, universities and researchers interested in the development area. The results show that 81% of current publications follow traditional approaches to data mining. The type of mining processes more study is Discovery 71% of the primary studies. These results are valuable for practitioners and researchers involved in the issue, and represent a major contribution to the are

Business process analysis based on anomaly detection in event logs: a study on an incident management case

Business processes allow anomalies to occur during execution. Anomaly detection aims to discover behaviors that are not typical or expected in the business process. In fact, early detection helps prevent intrusion and other risks in companies. There are several approaches that address this problem in process mining. This paper discusses anomaly detection approaches in business process discovery using a real-world event log from an ITIL-covered incident management process. We discuss benefits and limitations of using knowledge from process models discovered after treating anomalies

A systematic mapping study of process mining

<p>This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced ‘discovery’ (71%) as the main type of process mining addressed and ‘categorical prediction’ (25%) as the main mining task solved. The most applied traditional technique is the ‘graph structure-based’ ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are ‘evolutionary computation’ (9%) and ‘decision tree’ (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.</p

SaminBot: un asistente virtual para recolectar datos durante la pandemia del COVID-19

The lack of datasets to make decisions in quick actions during the pandemic caused by COVID-19 showed the need to use new technologies to speed up the process of capturing decentralized information. In this article, we present a virtual assistant (“chatbot”) called SaminBot, as an alternative to collect data and provide information during the COVID-19 pandemic, this chatbot was applied in the Cusco-Peru region with conversations in the areas of health, economy and education from January to August 2020. “SaminBot” starts data collection based on the user’s area of interest, obtains demographic information, and guides him through questions with the intention of providing useful information according to his personal situation. The questionnaires were validated by specialists according to its major. Data gathering was performed from January to June 2021 through WhatsApp and Facebook Messenger platforms as well the project’s website where 1,586 records were obtained.La falta de base de datos para tomar decisiones en acciones rápidas durante la pandemia ocasionada por el COVID-19, mostraron la necesidad de usar nuevas tecnologías para agilizar el proceso de captura de información descentralizada. Este artículo presenta un asistente virtual (“chatbot”) denominado “SaminBot”, como una alternativa para recolectar datos y brindar información durante la pandemia del COVID-19, este chatbot se aplicó en la región del Cusco-Perú con conversaciones en las áreas de salud, economía y educación de enero a agosto del 2020. “SaminBot” inicia la recolección de datos en función del área de interés del usuario, obtiene información demográfica del mismo y lo va guiando a través de preguntas con la intención de proveerle información útil de acuerdo con su situación personal. Los cuestionarios fueron validados por especialistas de acuerdo a su campo, el proceso de recolección de datos inició en Enero y finalizó en Junio del 2021 mediante las plataformas WhatsApp, Facebook Messenger y la página web donde se obtuvo 1586 registros

Systematic mapping study on process mining in agile software development

18th International Conference on Software Process Improvement and Capability Determination, SPICE 2018; Tessaloniki; Greece; 9 October 2018 through 10 October 2018Process mining is a process management technique that allows for the analysis of business processes based on the event logs and its aim is to discover, monitor and improve executed processes by extracting knowledge from event logs readily available in information systems. The popularity of agile software development methods has been increasing in the software development field over the last two decades and many software organizations develop software using agile methods. Process mining can provide complementary tools to Agile organizations for process management. Process mining can be used to discover agile processes followed by agile teams to establish the baselines and to determine the fidelity or they can be used to obtain feedback to improve agility. Despite the potential benefit of using process mining for agile software development, there is a lack of research that systematically analyzes the usage of process mining in agile software development. This paper presents a systematic mapping study on usage of process mining in agile software development approaches. The aim is to find out the usage areas of process mining in agile software development, explore commonly used algorithms, data sources, data collection mechanisms, analysis techniques and tools. The study has shown us that process mining is used in Agile software development especially for the purpose of process discovery from task tracking applications. We also observed that source code repositories are main data sources for process mining, a diversity of algorithms are used for analysis of collected data and ProM is the most widely used analysis tool for process mining