Verification of Data-aware Business Processes in the Presence of Ontologies

TL;DR

This paper introduces a formal framework for verifying data-aware business processes with ontologies, combining high-level semantics, inconsistency handling, and context sensitivity to improve system correctness analysis.

Contribution

It proposes GKABs, a comprehensive formalism for semantic-rich DABP with ontologies, and develops verification techniques applicable to various system variants.

Findings

Verification reduces to existing Data-Centric Dynamic Systems techniques.

Framework supports inconsistency repair semantics.

Enables high-level conceptual analysis of process evolution.

Abstract

The meet up between data, processes and structural knowledge in modeling enterprise systems is a challenging task that has led to the study of combining formalisms from knowledge representation, database theory, and process management. To ensure system correctness, formal verification also comes into play and offers well-established techniques. In line with this, significant results have been obtained within the research on data-aware business processes (DABP), which studies the marriage between static and dynamic aspects of a system within a unified framework. Here, we investigate the verification of DABP in the presence of ontologies, and provide the following contributions: (1) We propose a formal framework called Golog-KABs (GKABs), by leveraging on the state of the art formalisms for DABP equipped with ontologies. GKABs enable us to specify semantically-rich DABP, where the system dynamics are specified using a high-level action language inspired by the Golog programming language. (2) We propose a parametric execution semantics for GKABs that is able to elegantly accommodate a plethora of inconsistency-aware semantics based on the well-known notion of repair, and this leads us to several variants of inconsistency-aware GKABs. (3) We enhance GKABs towards context-sensitive GKABs. (4) We introduce the so-called Alternating-GKABs that allow for a more fine-grained analysis over the systems. (5) We introduce a novel framework called Semantically-Enhanced Data-Aware Processes (SEDAPs) that, by utilizing ontologies, enable us to have a high-level conceptual view over the evolution of the underlying system. We also provide reductions for the verification of sophisticated first-order temporal properties over all of the settings above, and show that verification can be addressed using existing techniques developed for Data-Centric Dynamic Systems (a well-established DABP framework).