The ProTest project provides solutions for three essential challenges in the business process domain by:
a) focusing on ensuring the security of business process executions through anomaly detection;
b) enabling to measure and improve the performance of processes and process execution engines through benchmarking; and
c) creating novel process testing approaches to foster the correctness of todays flexible process model executions.
ProTest enables, for example, the prevention of fraudulent behavior in business process executions to foster the commercial success of organizations. In addition, it helps to assess the performance of todays process execution engines to pave the way towards high performance process model executions. Finally, it enables to ensure the correctness of process models to satisfy customer requirements and needs.
- Appendix for technical report Kristof Böhmer and Stefanie Rinderle-Ma: A systematic literature review on process testing: Approaches, challenges, and research directions. CoRR, http://arxiv.org/abs/1509.04076 (2015)
- Test data for the submission: Kristof Böhmer and Stefanie Rinderle-Ma: Automatic Signature Generation for Anomaly Detection in Business
Process Instance Data. Submitted to CaISE 2016. The data consists of three different data formats (EDIFACT, XML, and JSON) which, we found, are frequently used by todays business processes. For each data format a thousand data entries were generated. The EDIFACT message represents a realistic purchase order message. Each EDIFACT data entry consists of 14 different variables while the JSON/XML data entries consist of 4 different variables. Each variable holds realistic real life data such as real life names, company names, addresses, dates, GUIDS, phone numbers, bank account numbers, and so on. The data was used to generate signatures and artificial anomalies to evaluate and asses the anomaly detection performance of the presented novel anomaly detection approach.
- Supplementary Material: Anonymization algorithms for the paper "Control Flow Structure preservation during Process Fragment Anonymization" accepted at Coopis 2017
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