Process mining : fuzzy clustering and performance visualization

The goal of performance analysis of business processes is to gain insights into operational processes, for the purpose of optimizing them. To intuitively show which parts of the process might be improved, performance analysis results can be projected onto process models. This way, bottlenecks can quickly be identified and resolved. Unfortunately, for many operational processes, good models, describing the process accurately and intuitively are unavailable. Process mining, or more precisely, process discovery, aims at deriving such models from events logged by information systems. However many mining techniques assume that all events in an event log are logged at the same level of abstraction, which in practice is often not the case. Furthermore, many mining algorithms produce results that are hard to understand by process specialists. In this paper, we propose a simple clustering algorithm to derive a model from an event log, such that this model only contains a limited set of nodes and edges. Each node represents a set of activities performed in the process, but many nodes can refer to many activities and vice versa. Using the discovered model, which represents the process at a potentially high level of abstraction, we present two different ways to project performance information onto it. Using these performance projections, process owners can gain insights into the process under consideration in an intuitive way. To validate our approach, we apply our work to a real-life case from a Dutch municipality