The paper titled “Divide And Conquer: A Tool Framework for Supporting Decomposed Discovery in Process Mining” accepted for publication in The Computer Journal.
Process mining has been around for more than a decade now, and in that period several discovery algorithms have been introduced that work fairly well on average-sized event logs, that is, event logs that contain about 50 different activities. Nevertheless, these algorithms have problems dealing with big event logs, that is, event logs that contain 200 or more different activities. For this reason, a generic approach has been developed which allows such big problems to be decomposed into a series of smaller (say, average-sized or even smaller) problems. This approach offers formal guarantees for the results obtained by it, and makes existing algorithms also tractable for larger logs. As a result, discovery problems may become feasible, or may become easier to handle. This paper introduces a tool framework, called Divide and Conquer that fully supports this generic approach and that has been implemented in ProM 6. Using this novel framework, this paper demonstrates that significant speed-ups can be achieved for discovery. This paper also discusses the fact that decomposition may lead to different results, but that this may even turn out to have a positive effect.