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The Product Data Model (PDM) is an example of a data-centric approach tomodelling information-intensive business processes, which offers exibility andfacilitates process optimization. Because the approach is declarative innature, there may be multiple, alternative execution plans that can produce thedesired end product. To generate such plans, several heuristics have beenproposed in the literature. The contributions of this work are twofold: (i) wepropose new heuristics that capitalize on established techniques for optimizingdata-intensive work ows in terms of execution time and cost and transfer themto business processes; and (ii) we extensively evaluate the existing solutions.Our results shed light on the merits of each heuristic and show that our newheuristics can yield significant benefits.

Rank-based Heuristics for Optimizing the Execution of Product Data Models