Control-flow Discovery from Event Streams

Process Mining represents an important research field that connects Business Process Modeling and Data Mining. One of the most prominent task of Process Mining is the discovery of a control-flow starting from event logs. This paper focuses on the important problem of control-flow discovery starting from a stream of event data. We propose to adapt Heuristics Miner, one of the most effective control-flow discovery algorithms, to the treatment of streams of event data. Two adaptations, based on Lossy Counting and Lossy Counting with Budget, as well as a sliding window based version of Heuristics Miner, are proposed and experimentally compared against both artificial and real streams. Experimental results show the effectiveness of control-flow discovery algorithms for streams on artificial and real datasets.