Given a set of existing products in the market and a set of customer preferences, we set a price for a specific product selected from a pool of candidate products to launch to market to gain the most profit. A customer preference represents his/her basic requirements. The dynamic skyline of a customer preference identifies the products that the customer may purchase. Each time the price of a candidate product is adjusted, it needs to compete with all of the existing products to determine whether it can be one of the dynamic skyline products of some customer preferences. To compute in parallel, we use a Voronoi-Diagram-based partitioning method to separate the set of existing products and that of customer preferences into cells. For these cells, a large number of combinations can be generated. For each price under consideration of a candidate product, we process all the combinations in parallel to determine whether this candidate product can be one of the dynamic skyline products of the customer preferences. We then integrate the results to decide the price for each candidate product to achieve the most profit. To further improve the performance, we design two efficient pruning strategies to avoid computing all combinations. A set of experiments using real and synthetic datasets are performed and the experiment results reveal that the pruning strategies are effective.

给定市场上现有的产品集和客户的喜好，我们为从候选产品库中选择的特定产品定价，以投放市场以获取最大的利润。客户喜好代表他/她的基本要求。在动态的天际线客户偏好的标识客户可以购买的产品。每次调整候选产品的价格时，它都需要与所有现有产品竞争，以确定它是否可以成为某些客户偏好的动态天际线产品之一。为了并行计算，我们使用基于Voronoi-Diagram的分区方法将现有产品集和客户偏好集分开到单元中。对于这些单元，可以生成大量的组合。对于候选产品考虑的每个价格，我们并行处理所有组合以确定该候选产品是否可以成为客户偏好的动态天际线产品之一。然后，我们对结果进行积分，以确定每种候选产品的价格，以获取最大的利润。为了进一步提高性能，我们设计了两种有效的修剪策略来避免计算所有组合。使用真实和合成数据集进行了一组实验，实验结果表明修剪策略是有效的。