Enhancing Declare Maps Based on Event Correlations

Traditionally, most process mining techniques aim at discovering

procedural

process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The “open world” assumption used by declarative models makes it easier to handle this variability. However, initial attempts to

automatically discover declarative process models

result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use

correlations

to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find

discriminatory patterns

, identify

outliers

, and analyze

bottlenecks

(e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.