  Process mining extracts valuable insights from event data to help
organizations improve their business processes, which is essential for their
growth and success. By leveraging process mining techniques, organizations gain
a comprehensive understanding of their processes' execution, enabling the
discovery of process models, detection of deviations, identification of
bottlenecks, and assessment of performance. Compliance checking, a specific
area within conformance checking, ensures that the organizational activities
adhere to prescribed process models and regulations. Linear Temporal Logic over
finite traces ($LTL_{f}$ ) is commonly used for conformance checking, but it
may not capture all temporal aspects accurately. This paper proposes Metric
Temporal Logic over finite traces ($MTL_{f}$ ) to define explicit time-related
constraints effectively in addition to the implicit time-ordering covered by
$LTL_f$. Therefore, it provides a universal formal approach to capture
compliance rules. Moreover, we define a minimal set of generic $MTL_f$ formulas
and show that they are capable of capturing all the common patterns for
compliance rules. As compliance validation is largely driven by the data model
used to represent the event logs, we provide a mapping from $MTL_f$ to the
common data models we found in the literature to encode event logs, namely, the
relational and the graph models. A comprehensive study comparing various data
models and an empirical evaluation across real-life event logs demonstrates the
effectiveness of the proposed approach.
