.png){kind=icon}
Return
Data-Aware Declarative Process Mining with SAT
.png){kind=icon}
OA
AI
DOI：10.1145/3600106.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Processmodels can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multiperspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.
Keywords:
Process mining
SAT
alloy
multi-perspective models
declarative models
Journal
IF:
6.6
Papers:
1.5K
Citations:
6.2K
Organization
View more organizations
Cited Papers
Haematopoietic progenitor cells in an infant who developed pancytopenia following an extensive burn.png)
Burns
BurnsIF0
PREAI
ShareSaveShareSaveShareSaveShareSaveShareSaveComparisons of Lordotic Cervical Spine Curvatures to a Theoretical Ideal Model of the Static Sagittal Cervical SpineSpine
SpineIF0
PREAI
ShareSave
ShareSave View more