To allow the distribution of control and visibility of cross-organizational process models and to increase availability and performance of the processes, a process model can be fragmented into logically different parts and distributed in the enterprise architecture. Fragmentation algorithms and execution environments that connect the fragmented process model parts together, recreating the original process execution semantics, have been proposed in earlier works. However, a critical challenge that is left open is the ability to independently change the control structure of a process fragment. This is not trivial as changing the control structure of a specific fragment could break the global process execution. The global process overview is also not available anymore because it is fragmented in the enactment environment and many independent control structure changes could have already been done. In this paper, we describe an approach based on state reconstruction, which enables checking the admissibility of changes made to the control structure of a process fragment in a fragmented event-based process enactment environment.

Within process mining research, one of the most important fields of study is process discovery, which can be defined as the extraction of control-flow models from audit trails or information system event logs. The evaluation of discovered process models is an essential but difficult task for any process discovery analysis. With this paper, we propose a novel approach for evaluating discovered process models based on artificially generated negative events. This approach allows for the definition of a behavioral F-measure for discovered process models, which is the main contribution of this paper.

Ant colony optimization (ACO) can be applied to the data mining field to extract rule-based classifiers. The aim of this paper is twofold. On the one hand, we provide an overview of previous ant-based approaches to the classification task and compare them with state-of-the-art classification techniques, such as C4.5, RIPPER, and support vector machines in a benchmark study. On the other hand, a new ant-based classification technique is proposed, named AntMiner+. The key differences between the proposed AntMiner+ and previous AntMiner versions are the usage of the better performing MAX-MIN ant system, a clearly defined and augmented environment for the ants to walk through, with the inclusion of the class variable to handle multiclass problems, and the ability to include interval rules in the rule list. Furthermore, the commonly encountered problem in ACO of setting system parameters is dealt with in an automated, dynamic manner. Our benchmarking experiments show an AntMiner+ accuracy that is superior to that obtained by the other AntMiner versions, and competitive or better than the results achieved by the compared classification techniques.