Conformance checking techniques compare process models of organizational behavior with observed process executions to reveal their deviations. Traditional alignments concern individual activities and provide a single out of potentially infinitely many explanations for observed deviations. Skip alignments lift insights to subprocesses and provide all possible explanations. Though valuable for analysts and process mining tools, there exist no interpretations how likely these deviations are. In this paper, we introduce skip probabilities revealing how likely certain subprocesses deviate w.r.t. an event log of observed process executions. We show the formal derivation of this calculation and demonstrate the feasibility of its computation. By analyzing a realistic case, we empirically show that yet hidden process insights can be derived from skip probabilities and how they contribute to targeted process improvement.

A good process model is expected not only to reflect the behavior of the process, but also to be as easy to read and understand as possible. Because preferences vary across different applications, numerous measures provide ways to reflect the complexity of a model with a numeric score. However, this abundance of different complexity measures makes it difficult to select one for analysis. Furthermore, most complexity measures are defined only for BPMN or EPC, but not for workflow nets.This paper adapts existing complexity measures to the world of workflow nets. It then compares these measures with a set of properties originally defined for software complexity, as well as new extensions to it. We discuss the importance of the properties in theory by evaluating whether matured complexity measures should fulfill them or whether they are optional. We find that not all inspected properties are mandatory, but also demonstrate that the behavior of evolutionary process discovery algorithms is influenced by some of these properties. Our findings help analysts to choose the right complexity measure for their specific use-case.

Process models describe the desired or observed behaviour of organisations. In stochastic process mining, computational analysis of trace data yields process models which describe process paths and their probability of execution. To understand the quality of these models, and to compare them, quantitative quality measures are used. This research investigates model comparison empirically, using stochastic process models built from real-life logs. The experimental design collects a large number of models generated randomly and using process discovery techniques. Twenty-five different metrics are taken on these models, using both existing process model metrics and new, exploratory ones. The results are analysed quantitatively, making particular use of principal component analysis. Based on this analysis, we suggest three stochastic process model dimensions: adhesion, relevance and simplicity. We also suggest possible metrics for these dimensions, and demonstrate their use on example models.

In process mining, computational processing of sequential data allows the discovery and analysis of processes followed by organisations. These can be either explicitly understood processes, captured in documents or rules, or implicit process paths known in more informal or emergent ways. This paper examines a long-lived institution of historical interest, the Qing (1644-1911) Chinese civil service, using data assembled by historians on civil officials during the 19th century. Mapping the promotion process by following paths of officials through civil service postings helps illuminate the everyday operation of the institution and the society around it. Two distinctive features of this data set are that it records states, not events, and careers often include holding multiple concurrent roles. The combination is a poor match for existing process discovery techniques. We describe this structure as a state snapshot log, and present a new discovery technique, the State Snapshot miner, for constructing stochastic Petri net models from such logs. A case study shows its use in analysing promotion paths for elite graduates in the Qing civil service.

Stochastic process models are a type of model that explicitly include elements of probability in describing an organization, facilitating different modes of analysis and simulation. Having obtained models of an organizational process, say through process mining, using them well depends on understanding their quality, and being able to compare different models. There may not be a single optimal stochastic model for a process, but tradeoffs between models, decided by their intended use. Reasoning about trade-offs in a precise way requires quantitative measures, and an understanding of how these measures relate, including whether they capture independent underlying properties.This paper is an empirical investigation of measures for stochastic process models built from real-life logs. The experimental design assembles a large collection of models built both randomly and by discovery techniques. A wide spectrum of candidate measures, drawn from and inspired by the process mining literature, are applied using these models. Based on this analysis, three stochastic quality dimensions are proposed: adhesion, entropy and simplicity.