An I/O Optimal and Scalable Skyline Query Algorithm

• Published in: Flexible and Efficient Information Handling pp. 127 - 139
• Main Authors: Gao, Yunjun, Chen, Gencai, Chen, Ling, Chen, Chun
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2006; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; Near Neighbor; Pruning Strategy; Skyline Computation; Skyline Point; Skyline Query; Software; Conjugate gradient method; Nearest neighbour; Pareto distribution; Scalability; Query processing; Storage management; Cholesky method; Branch and bound method; Database; Data processing; Static method
• ISBN: 3540359699; 9783540359692
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11788911_11

Given a set of d-dimensional points, skyline query returns the points that are not dominated by any other point on all dimensions. Currently, BBS (branch-and-bound skyline) is the most efficient skyline processing method over static data in a centralized setting. Although BBS has some desirable features (e.g., I/O optimal and flexibility), it requires large main-memory consumption. In this paper, we present an improved skyline computation algorithm based on best-first nearest neighbor search, called IBBS, which captures the optimal I/O and less memory space (i.e., IBBS visits and stores only those entries that contribute to the final skyline). Its core enables several effective pruning strategies to discard non-qualifying entries. Extensive experimental evaluations show that IBBS outperforms BBS in both scalability and efficiency for most cases, especially in low dimensions.