p241-yuan - Efficient Computation of the Skyline Cube...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Efficient Computation of the Skyline Cube * Yidong Yuan † Xuemin Lin † Qing Liu † Wei Wang † Jeffrey Xu Yu § Qing Zhang † The University of New South Wales † The Chinese University of Hong Kong § { yyidong, lxue, qingl, weiw, qzhang } @cse.unsw.edu.au [email protected] Abstract Skyline has been proposed as an impor- tant operator for multi-criteria decision mak- ing, data mining and visualization, and user- preference queries. In this paper, we con- sider the problem of efficiently computing a Skycube , which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computa- tion algorithms can be immediately extended to computing each skyline query indepen- dently, such “shared-nothing” algorithms are inefficient. We develop several computation sharing strategies based on effectively iden- tifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute Skycube efficiently. Fi- nally, our extensive performance evaluations confirm the effectiveness of the sharing strate- gies. It is shown that new algorithms signifi- cantly outperform the na¨ ıve ones. 1 Introduction The skyline operator and its computation have at- tracted much attention recently. This is mainly due to the importance of skyline results in many applications, such as multi-criteria decision making [6], data mining and visualization [13], and user-preference queries [12]. A skyline query over d dimensions selects the points that are not dominated by any other points restricted to those dimensions. Consider a typical skyline query example as follows: a real estate company has a list of properties online, each with price , dist (distance to city), age , and bedroom number attributes. Assume there are five properties as listed in Figure 1(a). A * This work was supported by ARC Discovery grant (DP0346004 and DP0345710) and UNSW Goldstar Grant (PS07248). Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment. Proceedings of the 31st VLDB Conference, Trondheim, Norway, 2005 user, who is sensitive to price and dist , issues a skyline query on price and dist attributes. The result will be { P 3 , P 5 , P 2 } as shown in Figure 1(b). P 4 , for example, is not in the skyline result because it is farther away from city compared with P 3 and its price is higher than P 3 too; that is, P 4 is dominated by P 3 . The skyline query can greatly help user to narrow down the search range. Given the importance of skyline queries, many efficient skyline computation algorithms...
View Full Document

This note was uploaded on 03/01/2010 for the course ICT ... taught by Professor ... during the Three '10 term at University of Sydney.

Page1 / 12

p241-yuan - Efficient Computation of the Skyline Cube...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online