Anomaly Detection based on Control-flow Pattern of Parallel Business Processes

The purpose of this paper was to discover an anomalous-free business process model from event logs. The process discovery was conducted using a graph database, specifically using Neo4J tool involving trace clustering and data filtering processes. We also developed a control-flow pattern to address, AND relation between activities named parallel business process. The result showed that the proposed method improved the precision value of the generated business process model from 0.64 to 0.81 compared to the existing algorithm. The better outcome is constructed by applying trace clustering and data filtering to remove the anomaly on the event log as well as discovering parallel (AND) relation between activities

A survey of graph-based algorithms for discovering business processes

Algorithms of process discovery help analysts to understand business processes and problems in a system by creating a process model based on a log of the system. There are existing algorithms of process discovery, namely graph-based. Of all algorithms, there are algorithms that process graph-database to depict a process model. Those algorithms claimed that those have less time complexity because of the graph-database ability to store relationships. This research analyses graph-based algorithms by measuring the time complexity and performance metrics and comparing them with a widely used algorithm, i.e. Alpha Miner and its expansion. Other than that, this research also gives outline explanations about graph-based algorithms and their focus issues. Based on the evaluations, the graph-based algorithm has high performance and less time complexity than Alpha Miner algorithm

Deviation detection in clinical pathways based on business alignment

Several unexpected behaviors may occur during actual treatment of clinical pathways, which will have negative impact on the implementation and the future work. To increase the performance of current deviation detection algorithms, a method is presented according to business alignment, which can effectively detect the anomaly in the implementation of the clinical pathways, provide judgment basis for the intervention in the process of the clinical pathway implementation, and play a crucial role in improving the clinical pathways. Firstly, the noise in diagnosis and treatment logs of clinical pathways will be removed. Then, the synchronous composition model is constructed to embody the deviations between the actual process and the theoretical model. Finally, A ∗ algorithm is selected to search for optimal alignment. A clinical pathway for ST-Elevation Myocardial Infarction (STEMI) under COVID-19 is used as a case study, and the superiority and effectiveness of this method in deviation detection are illustrated in the result of experiments

Method for Repairing Process Models with Selection Structures Based on Token Replay

Enterprise information systems (EIS) play an important role in business process management. Process mining techniques that can mine a large number of event logs generated in EIS become a very hot topic. There always exist some deviations between a process model of EIS and event logs. Therefore, a process model needs to be repaired. For the process model with selection structures, the mining accuracy of the existing methods is reduced because of the additional self-loops and invisible transitions. In this paper, a method for repairing Logical-Petri-nets-based process models with selection structures is proposed. According to the relationship between the input and output places of a sub-model, the deviation position is determined by a token replay method. Then, some algorithms are designed to repair the process models based on logical Petri nets. Finally, the effectiveness of the proposed method is illustrated by some experiments, and the proposed method has relatively high fitness and precision compared with its peers

Benefícios da utilização da mineração de processos / Benefits of using process mining

A mineração de processos é um tema que tem apresentado relevância nos últimos anos, tendo em vista a constante necessidade de melhoria de processos de negócios em ambientes competitivos. Assim, num contexto em que se busca melhorias de eficiência, a presente pesquisa objetiva identificar os principais benefícios resultantes da utilização de mineração de processos nas organizações. Para se alcançar o objetivo proposto pelo estudo, foi realizada uma revisão sistemática de literatura, proposta por Cooper (1984), objetivando, a partir da pesquisa e análise dos estudos primários, realizar a discussão e a síntese dos resultados. A partir da metodologia utilizada, foram selecionados estudos primários que dispunham sobre os benefícios decorrentes da aplicação da mineração de processos. Foram identificados diversos benefícios decorrentes da utilização da técnica nas organizações, classificados em 14 modalidades distintas.O presente estudo se justifica pela relevância do tema e pela falta de achados, na literatura, de estudos que abordem, de forma sistemática, os benefícios identificados por pesquisas primárias, decorrentes da aplicação da mineração de processos.

Monitoring interactions across multi business processes with token carried data

The rapid development of web service provides many opportunities for companies to migrate their business processes to the Internet for wider accessibility and higher collaboration efficiency. However, the open, dynamic and ever-changing Internet also brings challenges in protecting these business processes. There are certain process monitoring methods and the recently proposed ones are based on state changes of process artifacts or places, however, they do not mention defending process interactions from outer tampering, where events could not be detected by process systems, or saving fault-handling time. In this paper, we propose a novel Token-based Interaction Monitoring framework based on token carried data to safeguard process collaboration and reduce problem solving time. Token is a more common data entity in processes than process artifacts and they cover all tasks’ executions. Comparing to detecting places’ state change, we set security checking points at both when tokens are just produced and to be consumed. This will ensure that even if data is tampered after being created it would be detected before being used