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Inferring Unobserved Events in Systems with Shared Resources and Queues

Fahland Dirk, Denisov Vadim, van der Aalst Wil. M.P.

Fundamenta Informaticae

2021

Vol. 183, nr 3-4

203--242

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety of reasons, real-life systems often record only a subset of all events taking place. To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system. We formulate and solve the problem by systematically introducing multi-entity concepts in event logs and process models. We introduce a partial-order based model of a multi-entity event log and a corresponding compositional model for multi-entity processes. We define PQR-systems as a special class of multi-entity processes with shared resources and queues. We then study the problem of inferring from an incomplete event log unobserved events and their timestamps that are globally consistent with a PQR-system. We solve the problem by reconstructing unobserved traces of resources and queues according to the PQR-model and derive bounds for their timestamps using a linear program. While the problem is illustrated for material handling systems like baggage handling systems in airports, the approach can be applied to other settings where recording is incomplete. The ideas have been implemented in ProM and were evaluated using both synthetic and real-life event logs.

Process Discovery and Conformance Checking Using Passages

van der Aalst W. M. P., Verbeek H. M. W.

Fundamenta Informaticae

2014

Vol. 131, nr 1

103--138

The two most prominent process mining tasks are process discovery (i.e., learning a process model from an event log) and conformance checking (i.e., diagnosing and quantifying differences between observed and modeled behavior). The increasing availability of event data makes these tasks highly relevant for process analysis and improvement. Therefore, process mining is considered to be one of the key technologies for Business Process Management (BPM). However, as event logs and process models grow, process mining becomes more challenging. Therefore, we propose an approach to decompose process mining problems into smaller problems using the notion of passages. A passage is a pair of two non-empty sets of activities (X, Y) such that the set of direct successors of X is Y and the set of direct predecessors of Y is X. Any Petri net can be partitioned using passages. Moreover, process discovery and conformance checking can be done per passage and the results can be aggregated. This has advantages in terms of efficiency and diagnostics. Moreover, passages can be used to distribute process mining problems over a network of computers. Passages are supported through ProM plug-ins that automatically decompose process discovery and conformance checking tasks.