Finding the most profitable candidate product by dynamic skyline and parallel processing

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.