Abstract
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 captured 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.
Highlights
The pervasive diffusion of digitization, which gained momentum thanks to advancements in electronics and computing at the end of the last century, has brought a wave of innovation in the tools supporting businesses and companies
To represent the time relationship between uncertain events, which can be in a partial order
The behavior graph carries the information regarding other types of uncertainty, like uncertain activity labels and indeterminate events. Such a representation is vital to establish which possible sequence of events in an uncertain trace most adhere to the behavior prescribed by a reference model, thereby enabling conformance checking; and to measure the number of possible occurrences of the directly-follows relationship between activities in an event log, making process discovery over uncertainty possible
Summary
The pervasive diffusion of digitization, which gained momentum thanks to advancements in electronics and computing at the end of the last century, has brought a wave of innovation in the tools supporting businesses and companies. Systems (PAISs)—useful to structurally support processes in a business—as well as research disciplines such as Business Process Management (BPM) and process mining. Process mining [1] is a field of research that enables process analysis in a data-driven manner. Process mining analyses are based on recordings of tasks and events in a process, memorize in an ensemble of information systems which support business operations. These recordings are exported and systematically collected in databases called event logs. Over 30 commercial software tools are available on the market for analyzing processes and their execution data. Process mining tools are used by process experts to analyze processes in tens of thousands of organizations, e.g., within Siemens, over 6000 employees actively use process mining to improve internal procedures
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