TY - GEN
T1 - AS-SIM
T2 - 26th International Symposium on Methodologies for Intelligent Systems, ISMIS 2022
AU - Bottrighi, Alessio
AU - Guazzone, Marco
AU - Leonardi, Giorgio
AU - Montani, Stefania
AU - Striani, Manuel
AU - Terenziani, Paolo
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Process model discovery has gained a lot of attention in recent years, to mine a process model from traces of process executions. In our recent work, we have proposed SIM (Semantic Interactive Miner), an innovative process mining tool able to discover the process model in an incremental way: first, a mining module builds an initial process model, called log-tree, from the available traces; then, such a model is refined interactively with domain experts, through merge and abstraction operations. However, in several contexts, traces are richer: they do not record only actions, but also states (i.e., values of parameters possibly affected by the actions). A typical example is the medical domain, where traces contain both actions and measurements of patients’ parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach aiming at discovering a comprehensive model, in which two distinct classes of nodes are considered, to capture both actions and states. We focus on the definition and on the discovery of the initial action-state process model (called action-state log-tree), while in our future work we will extend SIM’s merge and abstraction operations accordingly.
AB - Process model discovery has gained a lot of attention in recent years, to mine a process model from traces of process executions. In our recent work, we have proposed SIM (Semantic Interactive Miner), an innovative process mining tool able to discover the process model in an incremental way: first, a mining module builds an initial process model, called log-tree, from the available traces; then, such a model is refined interactively with domain experts, through merge and abstraction operations. However, in several contexts, traces are richer: they do not record only actions, but also states (i.e., values of parameters possibly affected by the actions). A typical example is the medical domain, where traces contain both actions and measurements of patients’ parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach aiming at discovering a comprehensive model, in which two distinct classes of nodes are considered, to capture both actions and states. We focus on the definition and on the discovery of the initial action-state process model (called action-state log-tree), while in our future work we will extend SIM’s merge and abstraction operations accordingly.
KW - Mining action+state evolution
KW - Process mining
KW - Process model discovery
UR - http://www.scopus.com/inward/record.url?scp=85140436503&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-16564-1_32
DO - 10.1007/978-3-031-16564-1_32
M3 - Conference contribution
AN - SCOPUS:85140436503
SN - 9783031165634
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 336
EP - 345
BT - Foundations of Intelligent Systems - 26th International Symposium, ISMIS 2022, Proceedings
A2 - Ceci, Michelangelo
A2 - Flesca, Sergio
A2 - Masciari, Elio
A2 - Manco, Giuseppe
A2 - Raś, Zbigniew W.
A2 - Raś, Zbigniew W.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 3 October 2022 through 5 October 2022
ER -