Abstract
This paper deals with the optimization of busi-ness processes (BP) verification by simplifying their equivalent algebraic expressions. Actual approaches of business processes verification use formal methods such as automated theorem proving and model checking to verify the accuracy of the business process design. Those processes are abstracted to mathematical models in order to make the verification task possible. However, the structure of those mathematical models is usually a Boolean expression of the business process variables and gateways. Thus leading to a combinatorial explosion when the number of literals is above a certain threshold. This work aims at optimizing the verification task by managing the problem size. A novel algorithm of Boolean simplification is proposed. It uses hypercube graph decomposition to find the minimal equivalent formula of a business process model given in its disjunctive normal form (DNF). Moreover, the optimization method is totally automated and can be applied to any business process having the same formula due to the independence of the Boolean simplification rules from the studied processes. This new approach has been numerically validated by comparing its performance against the state of the art method Quine-McCluskey (QM) through the optimization of several processes with various types of branching.
Highlights
Business processes are key assets of any organization or information system [1], [2]
The verification task is a crucial step between the modeling and the execution phases of any business processes (BP)
The Quine-McCluskey algorithm follows a down-top approach: it tries to find all prime implicants of size 2 size 4 and so on, which means that it wastes time on multiple partial prime implicants before reaching the optimum formula
Summary
Business processes are key assets of any organization or information system [1], [2]. They are the communication interface and the medium of exchange between the organization stakeholders [3]. The verification task is a crucial step between the modeling and the execution phases of any BP. The complexity of reallife BP and the use of automated modeling tools often lead to complex models called “spaghetti” process models [6], [7] where manual verification is difficult to perform [8]. Automatic verification includes: Model Checking (MC) [5], [9] and Automated Theorem Proving (ATP) [10], [11]
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