Fahland, D. (2022). Multi-dimensional Process Analysis. In C. Di Ciccio, R. Dijkman, A. del Río Ortega, & S. Rinderle-Ma (Eds.), Business Process Management – 20th International Conference, BPM 2022, Proceedings: Lecture Notes in Computer Science (Vol. 13420, pp. 27-33). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13420 LNCS). Springer. https://doi.org/10.1007/978-3-031-16103-2_3
Abstract
Processes are complex phenomena that emerge from the interplay of human actors, materials, data, and machines. Process science develops effective methods and techniques for studying and improving processes. The BPM field has developed mature methods and techniques for studying and improving process executions from the control-flow perspective, and the limitations of control-flow focused thinking are well-known. Current research explores concepts from related disciplines to study behavioral phenomena “beyond” control-flow. However, it remains challenging to relate models and concepts of other behavioral phenomena to the dominant control-flow oriented paradigm. This tutorial introduces several recently developed simple models that naturally describe behavior beyond control-flow, but are inherently compatible with control-flow oriented thinking. We discuss the Performance Spectrum to study performance patterns and their propagation over time, Event Knowledge Graphs to study networks of behavior over data objects and actors, and Proclets as a formal model for reasoning over control-flow, data object, queue and actor behavior. For each model, we discuss which phenomena can be studied, which insights can be gained, which tools are available, and to which other fields they relate.