TY - GEN
T1 - Process Mining Meets Statistical Model Checking
T2 - Workshops on AI4BPM, BP-Meet-IoT, BPI, BPM and RD, BPMS2, BPO, DEC2H, and NLP4BPM 2022, co-located with the 20th International Conference on Business Process Management, BPM 2022
AU - Casaluce, Roberto
AU - Burattin, Andrea
AU - Chiaromonte, Francesca
AU - Vandin, Andrea
N1 - Publisher Copyright:
© 2023, Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - We propose a novel research line integrating Statistical Model Checking (SMC), a family of simulation-based analysis techniques from quantitative formal methods, with Process Mining (PM), a collection of data-driven process-oriented techniques. SMC and PM are complementary. SMC focuses on performing the right number of simulations to obtain statistically-reliable estimations (e.g., the probability of success of an attack). PM focuses on reconstructing a model of a system using logs of its traces. Nevertheless, both approaches aim at providing evidence of issues/guarantees of the system, and at proposing enhancements. We aim at enriching SMC by explaining why it produced specific estimates. This might help, e.g., identifying issues in the model (validation) or suggesting improvements (enhancement). Given that SMC uses statistics to decide what is the correct number of simulations (or traces), we avoid by-construction the complex issue of under-representation of system behavior in the logs crucial to many PM exercises. This work-in-progress paper demonstrates the proposed methodology and its usefulness using a simple example from the security threat modeling domain. We show how PM helps highlighting both mistakes in the model, and possibilities for improvement.
AB - We propose a novel research line integrating Statistical Model Checking (SMC), a family of simulation-based analysis techniques from quantitative formal methods, with Process Mining (PM), a collection of data-driven process-oriented techniques. SMC and PM are complementary. SMC focuses on performing the right number of simulations to obtain statistically-reliable estimations (e.g., the probability of success of an attack). PM focuses on reconstructing a model of a system using logs of its traces. Nevertheless, both approaches aim at providing evidence of issues/guarantees of the system, and at proposing enhancements. We aim at enriching SMC by explaining why it produced specific estimates. This might help, e.g., identifying issues in the model (validation) or suggesting improvements (enhancement). Given that SMC uses statistics to decide what is the correct number of simulations (or traces), we avoid by-construction the complex issue of under-representation of system behavior in the logs crucial to many PM exercises. This work-in-progress paper demonstrates the proposed methodology and its usefulness using a simple example from the security threat modeling domain. We show how PM helps highlighting both mistakes in the model, and possibilities for improvement.
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U2 - 10.1007/978-3-031-25383-6_18
DO - 10.1007/978-3-031-25383-6_18
M3 - Conference contribution
AN - SCOPUS:85151046216
SN - 9783031253829
T3 - Lecture Notes in Business Information Processing
SP - 243
EP - 256
BT - Business Process Management Workshops - BPM 2022 International Workshops, Revised Selected Papers
A2 - Cabanillas, Cristina
A2 - Garmann-Johnsen, Niels Frederik
A2 - Koschmider, Agnes
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 11 September 2022 through 16 September 2022
ER -