A semantic approach for process annotation and similarity analysis

Abstract

Research in the area of process modeling and analysis has a long-established tradition. There are quite few formalism for capturing processes, which are also accompanied by a number of optimization approaches. We introduce a novel approach, which employs semantics, for process annotation and analysis. In particular, we distinguish between target processes and current processes. Target process models describe how a process should ideally run and define a framework for current processes, which in contrast, capture how processes actually run in reallife use cases. In some cases, current processes do not match the target process models and can even overhaul them. Therefore, one is interested in the similarity between the defined target process model and current processes. The comparisons can consider different characteristics of processes such as service quality measures and dimensions. Current solutions perform process mining methods to discover hidden structures or try to infer knowledge about processes by using specific ontologies. To this end, we propose a novel method to capture and formalize processes, employing semantics and devising strategies and similarity measures that exploit the semantic representation to calculate similarities between target and current processes. As part of the similarity analysis, we consider different service qualities and dimensions in order to determine how they influence the target process models.