Detecting self-conflicts for business action rules

Essential discrepancies in business operation datasets may cause failures in operational decisions. For example, an antecedent X may accidentally lead to different action results, which obviously violates the atomicity of business action rules and will possibly cause operational failures. These inconsistencies within business rules are called self-conflicts. In order to handle the problem, this paper proposes a fast rules conflict detection algorithm called Multiple Slot Parallel Detection (MSPD). The algorithm manages to turn the seeking of complex conflict rules into the discovery of non-conflict rules which can be accomplished in linear time complexity. The contributions include: (1) formally proposing the Self-Conflict problem of business action rules, (2) proving the Theorem of Rules Non-conflict, (3) proposing the MSPD algorithm which is based on Huffman- Tree, (4) conducting extensive experiments on various datasets from Civil Airport business rules sets, which shows that the proposed algorithm saves 33.6% more space than the traditional Policy tree algorithm and improved the detection speed by 36.2%.