ProCI
Process Conformance under Incomplete Information (2020-2023)
Process-aware information systems coordinate the execution of a set of elementary actions to reach a business goal, where actions may be as fine-grained as function calls or as coarsegrained as complex business transactions. The behaviour of such systems is commonly described by process models. However, once data is recorded during runtime, typically in the form of logs or streams of events, the question of conformance emerges: how do the modelled behaviour of a system and its recorded behaviour relate to each other? Answering this question is the basis for the detection, interpretation, and compensation of any deviation between a model of a process-oriented system and its actual execution.
Driven by trends such as process automation, data sensing, and large-scale instrumentation of process-related resources, the volume of event data and the frequency at which it is generated is increasing in today’s world. Post-mortem conformance checking based on a complete history of a process’ execution is thus no longer a viable option.
The PROCI project sets out to provide the foundations for conformance checking that breaks with the omnipresent assumption of comprehensive access to event data and enables reasoning under incomplete information. Specifically, models and algorithms will be developed for sampled and online conformance checking. Given the exponential time complexity of common conformance checking techniques, the former strives for drastic improvements in runtime by considering only a fraction of large volumes of event data. The latter targets the question of space efficiency when conformance checking is realised over streams of events rather than static logs. In either case, conformance checking will be lossy, grounded on an incomplete view on the event data of a process, and needs to update a description of deviations with each new sample of a log or batch of an event stream, respectively. The central research challenges therefore will be (1) to devise the formal foundations for reasoning about partial conformance results, (2) to devise algorithms for sampled and online conformance checking, giving statistical guarantees on the bias induced by data incompleteness, and (3) to achieve robustness against changes in underlying data distributions. Experimental validations will be performed to empirically demonstrate the achieved improvements in runtime and space efficiency compared to state-of-the-art algorithms that postulate complete access to event data.
Team:
- Prof. Dr. Matthias Weidlich
- Prof. Dr. Lars Grunske
- Lam Hoang Nguyen, M.Sc
- Martin Kabierski
ProCI is funded by the German Research Foundation / Deutsche Forschungsgemeinschaft (DFG).
Publications:
- Bauer, M., van der Aa, H., & Weidlich, M. (2019, September). Estimating process conformance by trace sampling and result approximation. In International Conference on Business Process Management (pp. 179-197). Springer, Cham.
- Nguyen, H. L., Nassar, N., Kehrer, T., & Grunske, L. (2020). MoFuzz: A fuzzer suite for testing model-driven software engineering tools. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 1103-1115). IEEE.
- Kabierski, M., Nguyen, H. L., Grunske, L., & Weidlich, M. (2021). Sampling What Matters: Relevance-guided Sampling of Event Logs. In 2021 3rd International Conference on Process Mining (ICPM) (pp. 64-71). IEEE.
- Nguyen, H. L., & Grunske, L. (2022). BeDivFuzz: Integrating Behavioral Diversity into Generator-based Fuzzing. In 44th IEEE/ACM International Conference on Software Engineering, ser. ICSE (Vol. 22).
- Bauer, M., van der Aa, H., & Weidlich, M. (2022). Sampling and approximation techniques for efficient process conformance checking. Information Systems, 104, 101666.