Process mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. There are two main drivers for the growing interest in process mining. On the one hand, more and more events are being recorded, thus, providing detailed information about the history of processes. On the other hand, there is a need to improve and support business processes in competitive and rapidly changing environments. This manifesto is created by the IEEE Task Force on Process Mining and aims to promote the topic of process mining. Moreover, by defining a set of guiding principles and listing important challenges, this manifesto hopes to serve as a guide for software developers, scientists, consultants, business managers, and end-users. The goal is to increase the maturity of process mining as a new tool to improve the (re)design, control, and support of operational business processes

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One of the main functions of process mining is the automated discovery of process models from event log files. However, in flexible environments, such as healthcare or customer service, delivering comprehensible process models can be very challenging, mainly due to the complexity of the registered logs. A prevalent response to this problem is trace clustering, that is, grouping behaviors and discovering a distinct model per group. In this paper, we propose a novel trace clustering technique inspired from the outranking relations theory. The proposed technique can handle multiple criteria with strongly heterogeneous scales, and it allows a non‐compensatory logic to guide the creation of a similarity metric. To reach this, we use three key components: We separate factors that are in favor of the similarity from those that are not, through discrimination thresholds; we provide non‐concordant factors with a “veto” power; and we aggregate all factors into an overall metric. We evaluated this novel, non‐compensatory approach against two of the most spotlighted trace clustering functions: variants' identification and model complexity reduction. Results suggest that the proposed technique can be used at both functions with compelling performance.

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