Incremental evaluation of top-k combinatorial metric skyline query

Design of Branch Definition Algorithm for Top-k Inverse Queries for Image Processing

Images are the main way for human beings to obtain and exchange information, and they play a crucial role in the process of human understanding and exploration of the world. Top-k inverse queries are widely used in real life. Currently, the most efficient algorithm for computing top-k inverse sets is the inverse top-k algorithm. Our algorithm is significantly limited when dealing with top-k inverse queries. To address these limitations, an intuitive branch-and-bound algorithm is proposed to efficiently handle top-k inverse queries, and novel optimization methods are discussed to mention its high performance. Experimental evaluation shows that the algorithm is far more efficient than the inverse top-k algorithm.

Identifying Most Preferential Skyline Product Combinations

Nowadays, department stores and online merchants usually develop some price promotion strategies to attract customers and increase their purchase intention. Therefore, it is significant for customers to pick out attractive products and obtain the maximum discount rate. Admittedly, the skyline query is a most useful tool to find out attractive products. However, it does little to help select the product combinations with the maximum discount rate. Motivated by this, we identify an interesting problem, a most preferential skyline product (MPSP) combination discovering problem, which is NP-hard, for the first time in the literature. This problem aims to report all skyline product combinations having the maximum discount rate. Since the exact algorithm for the MPSP is not scalable to large or high-dimensional datasets, we design an approximate algorithm that guarantees the accuracy of the results. The experiment results demonstrate the efficiency and effectiveness of our proposed algorithms.