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Business processes are vital to managing organizations as they sustain a company's competitiveness. Consequently, these organizations maintain collections of hundreds or thousands of process models for streamlining working procedures and facilitating process implementation. Yet, the management of large process model collections requires effective searching capabilities. Recent research focused on similarity search of process models, but querying process models is still a largely open topic. This article presents an approach to querying process models that takes a process example as input and discovers all models that allow replaying the behavior of the query. To this end, we provide a notion of behavioral inclusion that is based on trace semantics and abstraction. Additional to deciding a match, a closeness score is provided that describes how well the behavior of the query is represented in the model and can be used for ranking. The article introduces the formal foundations of the approach and shows how they are applied to querying large process model collections. An experimental evaluation has been conducted that confirms the suitability of the solution as well as its applicability and scalability in practice. [ABSTRACT FROM AUTHOR]

Highlights • A technique for performance-driven model reduction of GSPNs is proposed. • The technique relies on foldings that aggregate or eliminate performance information. • Foldings preserve model stability and have a bound for the introduced performance estimation error. • Given a budget for the estimation error, an optimal sequence of foldings can be found. Abstract Operational process models such as generalised stochastic Petri nets (GSPNs) are useful when answering performance questions about business processes (e.g. ‘how long will it take for a case to finish?’). Recently, methods for process mining have been developed to discover and enrich operational models based on a log of recorded executions of processes, which enables evidence-based process analysis. To avoid a bias due to infrequent execution paths, discovery algorithms strive for a balance between over-fitting and under-fitting regarding the originating log. However, state-of-the-art discovery algorithms address this balance solely for the control-flow dimension, neglecting the impact of their design choices in terms of performance measures. In this work, we thus offer a technique for controlled performance-driven model reduction of GSPNs, using structural simplification rules, namely foldings. We propose a set of foldings that aggregate or eliminate performance information. We further prove the soundness of these foldings in terms of stability preservation and provide bounds on the error that they introduce with respect to the original model. Furthermore, we show how to find an optimal sequence of simplification rules, such that their application yields a minimal model under a given error budget for performance estimation. We evaluate the approach with two real-world datasets from the healthcare and telecommunication domains, showing that model simplification indeed enables a controlled reduction of model size, while preserving performance metrics with respect to the original model. Moreover, we show that aggregation dominates elimination when abstracting performance models by preventing under-fitting due to information loss. [ABSTRACT FROM AUTHOR]

Business process models are an important means to design, analyze, implement, and control business processes. As with every type of conceptual model, a business process model has to meet certain syntactic, semantic, and pragmatic quality requirements to be of value. For many years, such quality aspects were investigated by centering on the properties of the model artifact itself. Only recently, the process of model creation is considered as a factor that influences the resulting model's quality. Our work contributes to this stream of research and presents an explorative analysis of the process of process modeling (PPM). We report on two large-scale modeling sessions involving 115 students. In these sessions, the act of model creation, i.e., the PPM, was automatically recorded. We conducted a cluster analysis on this data and identified three distinct styles of modeling. Further, we investigated how both task- and modeler-specific factors influence particular aspects of those modeling styles. Based thereupon, we propose a model that captures our insights. It lays the foundations for future research that may unveil how high-quality process models can be established through better modeling support and modeling instruction. [ABSTRACT FROM AUTHOR]