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Hybrid genetic algorithm and association rules for mining workflow best practices

, , and . Expert Systems with Applications 39 (12): 10544--10551 (September 2012)

Abstract

Business workflow analysis has become crucial in strategizing how to create competitive edge. Consequently, deriving a series of positively correlated association rules from workflows is essential to identify strong relationships among key business activities. These rules can subsequently, serve as best practices. We have addressed this problem by hybridizing genetic algorithm with association rules. First, we used correlation to replace support-confidence in genetic algorithm to enable dynamic data-driven determination of support and confidence, i.e., use correlation to optimize the derivation of positively correlated association rules. Second, we used correlation as fitness function to support upward closure in association rules (hitherto, association rules support only downward closure). The ability to support upward closure allows derivation of the most specific association rules (business model) from less specific association rules (business meta-model) and generic association rules (reference meta-model). Downward closure allows the opposite. Upward-downward closures allow the manager to drill-down and analyze based on the degree of dependency among business activities. Subsequently, association rules can be used to describe best practices at the model, meta-model and reference meta-model levels with the most general positively dependent association rules as reference meta-model. Experiments are based on an online hotel reservation system. ⺠Hybrid GA and association rule algorithm (AR) to simulate business process. ⺠Correlation replaces support and confidence in AR (providing upward closure). ⺠Correlation replaces fitness function in GA. ⺠Positively-correlated ARs denote highly dependent business processes, enhancing OLAP. ⺠Positively-correlated ARs as best practices refine strategic planning models, DSS.

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DOI:
10.1016/j.eswa.2012.02.183
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BibTeX key:
LimLeeRaman12
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