Analyzing Business Systems comprised of Rules and Processes using Decision Diagrams

Abstract

Modern Businesses are rapidly growing in complexity and functionalities. To ensure smooth functioning, businesses need to adhere to a set of guidelines and constraints which are efficiently represented by Business Rules(BRs). Due to the large number of inter-dependent BRs, anomalies such as inconsistencies, redundancies and circularities creep in to the rule base, which if not dealt with properly can cause the business to function improperly causing significant damage at multiple levels. Present state of the art methods identify such anomalies in BRs by converting the rules to knowledge representation (Ontology, SMT-LIBv2, etc.) and then running them on solvers. These approaches suffer from certain drawbacks, namely incomplete mappings and scalability of solvers. To overcome these shortcomings, in this paper we propose to represent the Business Rules(BRs) as Decision Diagrams (BDD, SDD, MDD, etc.) and use graph algorithms on top of their canonical representations to identify anomalies. Presently, business rules and processes are treated separately. We model rules as Decision Diagrams(DDs) to integrate with certain graphical representations of business processes (e.g., DCR Graphs, BPMN, etc.), enabling us to efficiently analyze a much more enriched set of business information. We show an initial set of mappings from business rules to Binary Decision Diagrams (BDD's), integrate with processes, identify various anomalies and outline our vision and prospective reach of this approach.