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In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, Petri nets form a theoretically well-explored description language and are therefore often used. In this paper, we extend the eST-Miner process discovery algorithm. The eST-Miner computes a set of Petri net places which are considered to be fitting with respect to a certain fraction of the behavior described by the given event log as indicated by a given noise threshold. It evaluates all possible candidate places using token-based replay. The set of replayable traces is determined for each place in isolation, i.e., these sets do not need to be consistent. This allows the algorithm to abstract from infrequent behavioral patterns occurring only in some traces. However, when combining places into a Petri net by connecting them to the corresponding uniquely labeled transitions, the resulting net can replay exactly those traces from the event log that are allowed by the combination of all inserted places. Thus, inserting places one-by-one without considering their combined effect may result in deadlocks and low fitness of the Petri net. In this paper, we explore adaptions of the eST-Miner, that aim to select a subset of places such that the resulting Petri net guarantees a definable minimal fitness while maintaining high precision with respect to the input event log. Furthermore, current place evaluation techniques tend to block the execution of infrequent activity labels. Thus, a refined place fitness metric is introduced and thoroughly investigated. In our experiments we use real and artificial event logs to evaluate and compare the impact of the various place selection strategies and place fitness evaluation metrics on the returned Petri net. [ABSTRACT FROM AUTHOR]

Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered Petri net aims to describe the life-cycle of individual cases. In reality, there is not one possible case notion, but multiple intertwined case notions. For example, events may refer to mixtures of orders, items, packages, customers, and products. A package may refer to multiple items, multiple products, one order, and one customer. Therefore, we need to assume that each event refers to a collection of objects, each having a type (instead of a single case identifier). Such object-centric event logs are closer to data in real-life information systems. From an object-centric event log, we want to discover an object-centric Petri net with places that correspond to object types and transitions that may consume and produce collections of objects of different types. Object-centric Petri nets visualize the complex relationships among objects from different types. This paper discusses a novel process discovery approach implemented in PM4Py. As will be demonstrated, it is indeed feasible to discover holistic process models that can be used to drill-down into specific viewpoints if needed. [ABSTRACT FROM AUTHOR]

Process discovery aims at constructing a model from a set of observations given by execution traces (a log). Petri nets are a preferred target model in that they produce a compact description of the system by exhibiting its concurrency. This article presents a process discovery algorithm using Petri net synthesis, based on the notion of region introduced by A. Ehrenfeucht and G. Rozenberg and using techniques from linear algebra. The algorithm proceeds in three successive phases which make it possible to find a compromise between the ability to infer behaviours of the system from the set of observations while ensuring a parsimonious model, in terms of fitness, precision and simplicity. All used algorithms are incremental which means that one can modify the produced model when new observations are reported without reconstructing the model from scratch. [ABSTRACT FROM AUTHOR]

The two most prominent process mining tasks are process discovery (i.e., learning a process model from an event log) and conformance checking (i.e., diagnosing and quantifying differences between observed and modeled behavior). The increasing availability of event data makes these tasks highly relevant for process analysis and improvement. Therefore, process mining is considered to be one of the key technologies for Business Process Management (BPM). However, as event logs and process models grow, process mining becomes more challenging. Therefore, we propose an approach to decompose process mining problems into smaller problems using the notion of passages. A passage is a pair of two non-empty sets of activities (X, Y) such that the set of direct successors of X is Y and the set of direct predecessors of Y is X. Any Petri net can be partitioned using passages. Moreover, process discovery and conformance checking can be done per passage and the results can be aggregated. This has advantages in terms of efficiency and diagnostics. Moreover, passages can be used to distribute process mining problems over a network of computers. Passages are supported through ProM plug-ins that automatically decompose process discovery and conformance checking tasks. [ABSTRACT FROM AUTHOR]