Abstract— The skyline search algorithm has recently emerged as an important technique in database research. Given a set of data points in a multidimensional database, such queries return points that are not “dominated” (detailed in the paper) by any other point. In practice, databases that require a skyline query usually provide numerous candidate dimensions, of which users are interested in only a few. As a result, queries are issued regarding various subsets of the dimensions and such queries are called subspace skyline queries. Using the conventional skyline algorithm to process these queries directly can be extremely ineffective. Additional algorithms and architectures have been added to improve search efficiency; however, such modifications can increase computational costs or necessitate an increase in data storage capacity. This paper proposes a novel index model based on a Gaussian function to enhance the performance of subspace skyline queries. Simulation results demonstrate the efficacy of the proposed tree in locating skyline points within a subspace.

Index Terms— Database, skyline, tree structure.

Y. C. Chen and C. Lee are with the Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, 701, Taiwan, R.O.C. (e-mail: mitsukoshi901@dblab.csie.ncku.edu.tw, leec@mail.ncku.edu.tw).

Cite: Yi-Chung Chen and Chiang Lee, " G-Tree: A Novel Index Structure for Subspace Skyline Query," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 3, no. 6, pp. 483-487, 2013.