Comparative Analysis of Process Mining Tools

In the current context of availability of large amounts of data (Big Data), its underlying value can be, frequently, devalued. However, there are several tools that allow to extract knowledge from data. Among other information, this knowledge can lead to improve processes or detect any failure during their execution. This work intends to compare several process mining (PM) tools, using different techniques. For each tool, the best scenario in the discovery of processes is found and the respective results are evaluated. The results showed that Disco is the simplest and most intuitive tool to use. Along with ProM, it also allows a complete analysis, without the need for theoretical knowledge concerning PM or programming. PM4Py, on the other hand, is a free framework that allows great customizations for all functionalities. So it is ideal for professionals with knowledge in PM needing more adjusted implementation or integration with other applications. From a cost perspective, either PM4Py or ProM are free. The use of PM4Py can be complemented by ProM for compliance verification

Online predicting conformance of business process with recurrent neural networks

Conformance Checking is a problem to detect and describe the differences between a given process model representing the expected behaviour of a business process and an event log recording its actual execution by the Process-aware Information System (PAIS). However, such existing conformance checking techniques are offline and mainly applied for the completely executed process instances, which cannot provide the real-time conformance-oriented process monitoring for an on-going process instance. Therefore, in this paper, we propose three approaches for online conformance prediction by constructing a classification model automatically based on the historical event log and the existing reference process model. By utilizing Recurrent Neural Networks, these approaches can capture the features that have a decisive effect on the conformance for an executed case to build a prediction model and then use this model to predict the conformance of a running case. The experimental results on two real datasets show that our approaches outperform the state-of-the-art ones in terms of prediction accuracy and time performance

Cost-based fitness in conformance checking

Experience in business process analysis shows that operational processes often do not conform to process models. Although classical conformance checking techniques can identify deviations of process executions from predefined models, they may produce inaccurate results due to strong assumptions. In this paper, we present a robust conformance checking technique based on Petri net techniques allowing us to lift assumptions and to take into account the cost of deviating from given models

B.F.v.: Cost-based Fitness in Conformance Checking

Abstract-Experience in business process analysis shows that operational processes often do not conform to process models. Although classical conformance checking techniques can identify deviations of process executions from predefined models, they may produce inaccurate results due to strong assumptions. In this paper, we present a robust conformance checking technique based on Petri net techniques allowing us to lift assumptions and to take into account the cost of deviating from given models