Process Mining over Unordered Event Streams

Abstract

Process mining is no longer limited to the one-off analysis of static event logs extracted from a single enterprise system. Rather, process mining may strive for immediate insights based on streams of events that are continuously generated by diverse information systems. This requires online algorithms that, instead of keeping the whole history of event data, work incrementally and update analysis results upon the arrival of new events. While such online algorithms have been proposed for several process mining tasks, from discovery through conformance checking to time prediction, they all assume that an event stream is ordered, meaning that the order of event generation coincides with their arrival at the analysis engine. Yet, once events are emitted by independent, distributed systems, this assumption may not hold true, which compromises analysis accuracy. In this paper, we provide the first contribution towards handling unordered event streams in process mining. Specifically, we formalize the notion of out-of-order arrival of events, where an online analysis algorithm needs to process events in an order different from their generation. Using directly-follows graphs as a basic model for many process mining tasks, we provide two approaches to handle such unorderedness, either through buffering or speculative processing. Our experiments with synthetic and real-life event data show that these techniques help mitigate the accuracy loss induced by unordered streams.

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