2 research outputs found
Secure Multi-Party Computation for Inter-Organizational Process Mining
Process mining is a family of techniques for analysing business processes
based on event logs extracted from information systems. Mainstream process
mining tools are designed for intra-organizational settings, insofar as they
assume that an event log is available for processing as a whole. The use of
such tools for inter-organizational process analysis is hampered by the fact
that such processes involve independent parties who are unwilling to, or
sometimes legally prevented from, sharing detailed event logs with each other.
In this setting, this paper proposes an approach for constructing and querying
a common type of artifact used for process mining, namely the frequency and
time-annotated Directly-Follows Graph (DFG), over multiple event logs belonging
to different parties, in such a way that the parties do not share the event
logs with each other. The proposal leverages an existing platform for secure
multi-party computation, namely Sharemind. Since a direct implementation of DFG
construction in Sharemind suffers from scalability issues, the paper proposes
to rely on vectorization of event logs and to employ a divide-and-conquer
scheme for parallel processing of sub-logs. The paper reports on an
experimental evaluation that tests the scalability of the approach on real-life
logs.Comment: 15 pages ,5 figure
Fairness-Aware Process Mining
Process mining is a multi-purpose tool enabling organizations to improve
their processes. One of the primary purposes of process mining is finding the
root causes of performance or compliance problems in processes. The usual way
of doing so is by gathering data from the process event log and other sources
and then applying some data mining and machine learning techniques. However,
the results of applying such techniques are not always acceptable. In many
situations, this approach is prone to making obvious or unfair diagnoses and
applying them may result in conclusions that are unsurprising or even
discriminating (e.g., blaming overloaded employees for delays). In this paper,
we present a solution to this problem by creating a fair classifier for such
situations. The undesired effects are removed at the expense of reduction on
the accuracy of the resulting classifier. We have implemented this method as a
plug-in in ProM. Using the implemented plug-in on two real event logs, we
decreased the discrimination caused by the classifier, while losing a small
fraction of its accuracy