An efficient algorithm for skyline queries in cloud computing environments

• Published in: China communications Vol. 15; no. 10; pp. 182 - 193
• Main Authors: Huang, Zhenhua, Xu, Weicheng, Cheng, Jiujun, Ni, Juan
• Format: Journal Article
• Language: English
• Published: China Institute of Communications 01.10.2018; School of Computer Science, South China Normal University, Guangzhou 510631, China; Department of Computer Science and Technology, Tongji University, Shanghai 201804, China%Department of Computer Science and Technology, Tongji University, Shanghai 201804, China%School of Computer Science, South China Normal University, Guangzhou 510631, China
• Subjects: Approximation algorithms; Cloud computing; Computational modeling; Lattices; Query processing; Simulated annealing; skyline query; steiner tree; Steiner trees; steiner tree; simulated annealing; cloud computing; skyline query
• ISSN: 1673-5447
• DOI: 10.1109/CC.2018.8485480

Skyline query processing has recently received a lot of attention in database and data mining communities. However, most existing algorithms consider how to efficiently process skyline queries from base tables. Obviously, when the data size and the number of skyline queries increase, the time cost of skyline queries will increase exponentially, which will seriously influence the query efficiency. Motivated by the above, in this paper, we consider improving the query efficiency via skyline views and propose a cost-based algorithm (abbr. CA) to efficiently select the optimal set of skyline views for storage. The CA algorithm mainly includes two phases: (i) reduce the skyline views selection to the minimum steiner tree problem and obtain the approximate optimal set AOS of skyline views, and (ii) adjust AOS and produce the final optimal set FOS of skyline views based on the simulated annealing. Moreover, in order to improve the extendibility of the CA algorithm, we implement it based on the map/reduce distributed computation model in cloud computing environments. The detailed theoretical analyses and extensive experiments demonstrate that the CA algorithm is both efficient and effective.