Process mining of test processes : a case study

Process mining techniques attempt to extract non-trivial and useful information from event logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities. Moreover, using conformance checking it is possible to investigate and quantify deviations between the real process and the modeled process. Several successful case studies have been reported in literature, all demonstrating the applicability of process mining. However, these case studies refer to rather structured administrative processes. In this paper, we investigate the applicability of process mining to less structured processes. We report on a case study where the ProM framework has been applied to the test processes of ASML (the leading manufacturer of wafer scanners in the world). This case study provides many interesting insights. On the one hand, process mining is also applicable to the less structured processes of ASML. On the other hand, the case study also shows the need for alternative mining approaches able to better visualize processes and provide more insights

A BIBLIOMETRIC REVIEW OF PROCESS MINING IN MANUFACTURING PROCESSES

The process mining is essential when it is needed to know what is happening in the execution of the processes, even more when it is manufacturing processes. Also, there is a lack of studies on process mining in manufacturing processes, one reason is because the process mining still a young field of study. Therefore, this paper presents a bibliometric review of a history of the process mining techniques applied in manufacturing processes. It was used, as methodology, a systematic review of the literature, by consulting databases from Web of Science and Scopus, using filters to generate a sample that would match with the scope of the research. As results, it was made some analysis, which presents the identification of the years, authors, research source, countries, number of citations, keywords cloud and industry type used in the cases studied. Also, some graphics, tables and information are presented on this paper

ProM : the process mining toolkit

Nowadays, all kinds of information systems store detailed information in logs. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect discrepancies between a modeled process (as it was envisioned to be) and an event log (as it actually is). Third, it can enrich an existing model with knowledge derived from an event log. This paper presents our tool ProM, which is the world-leading tool in the area of process mining

XES, XESame, and ProM 6

Process mining has emerged as a new way to analyze business processes based on event logs. These events logs need to be extracted from operational systems and can subsequently be used to discover or check the conformance of processes. ProM is a widely used tool for process mining. In earlier versions of ProM, MXML was used as an input format. In future releases of ProM, a new logging format will be used: the eXtensible Event Stream (XES) format. This format has several advantages over MXML. The paper presents two tools that use this format - XESame and ProM 6 - and highlights the main innovations and the role of XES. XESame enables domain experts to specify how the event log should be extracted from existing systems and converted to XES. ProM 6 is a completely new process mining framework based on XES and enabling innovative process mining functionality