Process Mining over Unordered Event Streams
Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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 confor mance 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.
Description
Keywords
Process mining, event streams, unordered streams
Citation
Awad, A. et al. (2020) “Process Mining over Unordered Event Streams,” in 2020 2nd International Conference on Process Mining (ICPM), pp. 81–88.