Scientific workflows for process mining: building blocks, scenarios, and implementation

Abstract

Over the past decade process mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form process instances. Process mining results tend to be very different from classical data mining results, e.g., process discovery may yield end-to-end process models capturing different perspectives rather than decision trees or frequent patterns. A process-mining tool like ProM provides hundreds of different process mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process mining should be repeatable and automated. Therefore, we propose a framework to support the analysis of process mining workflows. Existing scientific workflow systems and data mining tools are not tailored towards process mining and the artifacts used for analysis (process models and event logs). This paper structures the basic building blocks needed for process mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM, a tool supporting scientific workflows for process mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.B Wil M. P. van der Aalst

Automated Discovery of Process Models from Event Logs: Review and Benchmark

“…In line with the above definition, we use k-fold crossvalidation [112] to measure event logs. This k-fold crossvalidation approach to measure generalization has been advocated in several studies in the field of automated process discovery [2], [113], [114], [115]. Concretely, we divide the log into k parts, we discover a model from k − 1 parts (i.e., we hold-out one part), and measure the fitness of the discovered model against the part held out.…”

Event stream-based process discovery using abstract representations

“…Some of the implementations are ported to RapidProM [3] (http://www.rapidprom.org), i.e. a plugin of RapidMiner (http://www.rapidminer.com), which allows for designing large-scale repetitive experiments by means of scientific workflows [10]. Source code of the implementations is available via the Stream-related packages within the ProM code base, i.e.…”

Guided Process Discovery – A pattern-based approach

“…Our method is implemented both in the open-source process mining framework ProM and as set of operators in the RapidMiner extension RapidProM [18].…”