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A Family of Case Studies on Business Process Mining

Business processes, most of which are automated by information systems, have become a key asset in organizations. Unfortunately, uncontrolled maintenance implies that information systems age over time until they need to be modernized. During software modernization, ageing systems cannot be entirely discarded because they gradually embed meaningful business knowledge, which is not present in any other artifact. This paper presents a technique for recovering business processes from legacy systems in order to preserve that knowledge. The technique statically analyzes source code and generates a code model, which is later transformed by pattern matching into a business process model. This technique has been validated over a two year period in several industrial modernization projects. This paper reports the results of a family of case studies that were performed to empirically validate the technique using analysis and meta-analysis techniques. The study demonstrates the effectiveness and efficiency of the technique.

Event Correlation in Non-Process-Aware Systems

Since business processes supported by traditional systems are implicitly defined, correlating events into the appropriate process instance is not trivial. This challenge is known as the event correlation problem. This paper presents an adaptation of an existing event correlation algorithm and incorporates it into a technique to collect event logs from the execution of traditional information systems. The technique first instruments the source code to collect events together with some candidate correlation attributes. Secondly, the algorithm is applied to the dataset of events to discover the best correlation conditions. Event logs are then built using such conditions. The technique has been semi-automated to facilitate its validation through an industrial case study involving a writer management system and a healthcare evaluation system. The study demonstrates that the technique is able to discover the correlation set and obtain well-formed event logs enabling business process mining techniques to be applied to traditional information systems.