Process Mining Handbook

The declarative specification of business processes is based upon the elicitation of behavioural rules that constrain the legal executions of the process. The carry-out of the process is up to the actors, who can vary the execution dynamics as long as they do not violate the constraints imposed by the declarative model. The constraints specify the conditions that require, permit or forbid the execution of activities, possibly depending on the occurrence (or absence) of other ones. In this chapter, we review the main techniques for process mining using declarative process specifications, which we call declarative process mining. In particular, we focus on three fundamental tasks of (1) reasoning on declarative process specifications, which is in turn instrumental to their (2) discovery from event logs and their (3) monitoring against running process executions to promptly detect violations. We ground our review on Declare, one of the most widely studied declarative process specification languages. Thanks to the fact that Declare can be formalized using temporal logics over finite traces, we exploit the automata-theoretic characterization of such logics as the core, unified algorithmic basis to tackle reasoning, discovery, and monitoring. We conclude the chapter with a discussion on recent advancements in declarative process mining, considering in particular multi-perspective extensions of the original approach.

Process models are among the milestones for Business Process Management and Mining, and used to describe a business process or to prescribe how its instances should be carried out. It follows that they need to fulfill certain properties to be useful. If they aim to represent how the process is currently being executed, they need to be precise and recall the behavior observed in reality. If the goal is to ensure that the process is executed according to laws and regulations, its model should only allow the behavior that is valid from a domain viewpoint and provides some guarantee to ensure good performance level. Process enhancement is the type of Process Mining that aims at models that fulfill these properties, and the literature further splits it into two subfields: process extension and process improvement. Process extension aims to incorporate the process perspectives on data, decision, resources and time into the model: their inclusion in process models enable designers to fine-tune the model specifications, thus obtaining models with higher levels of precision. Process improvement passes through an “improved” process model. If the model contains portions of behavior that lead to unsatisfactory outcomes (high costs, low customer satisfactions, etc.) or that violate norms and regulations, one would like those portions to be disallowed by the model. In case some executions are observed in reality and are not allowed by the model, they should be incorporated into the model if they are observed to generally yield good performances. This chapter discusses these two types of process enhancement, and illustrates some basic and some advanced techniques to tackle it, highlighting the pros and cons, and the underlaying assumptions.