In software defect prediction, predictive models are estimated based on various code attributes to assess the likelihood of software modules containing errors. Many classification methods have been suggested to accomplish this task. However, association based classification methods have not been...

In this report, we present a ProM plugin which allows for straightforward event log generation, using token-based simulation driven by Petri net models. Although a large number of tools already exist for the simulation and analysis of Petri nets (CPN Tools being among the most notable), no...

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess...

Process mining is the research area that is concerned with knowledge discovery from event logs and is often situated at the intersection of the fields of data mining and business process management. Although the term entails a collection of a-posteriori analysis methods for extracting knowledge...

Recent years have witnessed the ability to gather an enormous amount of data in a large number of domains. Also in the field of business process management, there exists an urgent need to beneficially use these data to retrieve actionable knowledge about the actual way of working in the context...

The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using...