A Literature Review on Business Process Management

Business Process (BP) is a set of coordinated tasks that define how to achieve organizational goals. It emerges as an efficient tool, whose main goal is supporting the design, administration, setup, disclosure and analysis of business processes, and organizations use it to identify opportunities to reduce costs, increase service or product quality, etc. The goal of BPM is to manage business processes. Organizations wish to manage perfectly these processes instead of fixing the non-ideal process setups or outcomes in a reactive manner. At present, variability management in the business processes domain is considered as a key of reuse. Process mining offers a set of techniques that retrieves information from event logs and gives companies a better understanding of their processes. Process mining has gained significant attention in both research and industry as a range of data mining tools has emerged. In this study, we will provide a systematic literature review from 2017 to 2021; we will use Kitchenham method to conduct this SLR. Data source as IEEE, ACM, Springer and ScienceDirect are used to obtain literature. We had, as a result, 51 papers from 3079 papers to complete this paper. This SLR had for objective to see the research trend on the topics of business process management, improvement, modeling and approaches using data mining

Conformance Checking-based Concept Drift Detection in Process Mining

One of the main challenges of process mining is to obtain models that represent a process as simply and accurately as possible. Both characteristics can be greatly influenced by changes in the control flow of the process throughout its life cycle. In this thesis we propose the use of conformance metrics to monitor such changes in a way that allows the division of the log into sub-logs representing different versions of the process over time. The validity of the hypothesis has been formally demonstrated, showing that all kinds of changes in the process flow can be captured using these approaches, including sudden, gradual drifts on both clean and noisy environments, where differentiating between anomalous executions and real changes can be tricky

Comparing concept drift detection with process mining tools

Organisations have seen a rise in the volume of data correspondingto business processes being recorded. Handling process data is ameaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, businessprocesses are subject to changes during their executions, addingcomplexity to their analysis. This paper aims at evaluating currently available Process Mining tools that handle concept drifts, i.e.changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these toolsbriefly comparing their differences, advantages, and disadvantages