Automatic derivation of conceptual database models from differently serialized business process models

Abstract

The existing tools that aim to derive data models from business process models are typically able to process the source models represented by one single notation and also serialized in one specific way. However, the standards (e.g., BPMN) enable different serialization formats and also provide serialization flexibility, which leads to various implementations of the standard in different modeling tools and results in differently serialized models in practice, which therefore significantly constraints usability of the existing model-driven tools. In this article, we present an approach to automatic derivation of conceptual database models from business process models represented by different notations, with particular focus on differently serialized process models. A deterministic rule-based approach is proposed to overcome the serialization specificities and to enable extraction of characteristic elements from differently serialized process models. Based on the proposed approach, we implemented an online web-based model-driven tool named AMADEOS, which is able to automatically derive conceptual database models from process models represented by different notations and also differently serialized. The experimental results show that the proposed approach and implemented tool enable successful extraction of specific elements from differently serialized process models and enable derivation of the target conceptual database models with very high completeness and precision.