2 research outputs found
Efficient Time and Space Representation of Uncertain Event Data
Process mining is a discipline which concerns the analysis of execution data
of operational processes, the extraction of models from event data, the
measurement of the conformance between event data and normative models, and the
enhancement of all aspects of processes. Most approaches assume that event data
is accurately capture behavior. However, this is not realistic in many
applications: data can contain uncertainty, generated from errors in recording,
imprecise measurements, and other factors. Recently, new methods have been
developed to analyze event data containing uncertainty; these techniques
prominently rely on representing uncertain event data by means of graph-based
models explicitly capturing uncertainty. In this paper, we introduce a new
approach to efficiently calculate a graph representation of the behavior
contained in an uncertain process trace. We present our novel algorithm, prove
its asymptotic time complexity, and show experimental results that highlight
order-of-magnitude performance improvements for the behavior graph
construction.Comment: 34 pages, 16 figures, 5 table
Partial order resolution of event logs for process conformance checking
While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably