论文标题
基于等级的启发式方法来优化产品数据模型的执行
Rank-based Heuristics for Optimizing the Execution of Product Data Models
论文作者
论文摘要
产品数据模型（PDM）是一种以数据为中心的方法来建模信息密集型业务流程的示例，该方法提供了质疑并促进了过程优化。由于该方法本质上是声明性的，因此可能会有多种替代性执行计划可以产生所需的最终产品。为了制定这样的计划，文献中已经提出了几种启发式方法。这项工作的贡献是双重的：（i）我们提出了新的启发式方法，以利用既定的技术来优化数据密集型工作，从而在执行时间和成本方面，并将其转移到业务流程中； （ii）我们广泛评估现有解决方案。我们的结果阐明了每种启发式方法的优点，并表明我们的新启发式方法可以带来巨大的好处。
The Product Data Model (PDM) is an example of a data-centric approach to modelling information-intensive business processes, which offers exibility and facilitates process optimization. Because the approach is declarative in nature, there may be multiple, alternative execution plans that can produce the desired end product. To generate such plans, several heuristics have been proposed in the literature. The contributions of this work are twofold: (i) we propose new heuristics that capitalize on established techniques for optimizing data-intensive work ows in terms of execution time and cost and transfer them to business processes; and (ii) we extensively evaluate the existing solutions. Our results shed light on the merits of each heuristic and show that our new heuristics can yield significant benefits.
