Event stream-based process discovery using abstract representations

Sebastiaan J Van Zelst,Boudewijn F Van Dongen,Wil M P Van Der Aalst

doi:10.1007/s10115-017-1060-2

Sebastiaan J Van Zelst, Boudewijn F Van Dongen + Show 1 more

Open Access

https://doi.org/10.1007/s10115-017-1060-2

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Abstract

The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM (http://promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

Highlights

Process mining [1] aims at understanding and improving business processes
Several instantiations of the architecture have been implemented in the process mining toolkits ProM [8] and RapidProM [9,10]
We presented a generic architecture that allows for adopting existing process discovery algorithms in an event stream setting

Summary

Introduction

Process mining [1] aims at understanding and improving business processes. The field consists of three main branches, i.e. process discovery, conformance checking and process enhancement. Process discovery aims at discovering a process model based on event data. Several process discovery algorithms exist [2,3,4,5,6,7] These algorithms all use an event log as an input. An event log is a static data source describing sequences of executed business process activities recorded over a historical time span. Conventional process discovery techniques are not able to cope with such large data sets, i.e. they fail when the data do not fit main memory. Since existing process discovery techniques use static data, they are not able to capture the dynamics of such event streams in an adequate manner. If for element e, B(e) = 0, we omit e from the multiset notation. Element inclusion applies to multisets, i.e. if e ∈ X and B(e) > 0 e ∈ B

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Discussion

Conclusion