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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (2): 99-117    DOI: 10.1631/jzus.C10a0728
    
PRISMO: predictive skyline query processing over moving objects
Nan Chen, Li-dan Shou, Gang Chen, Yun-jun Gao, Jin-xiang Dong
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; China National Tobacco Corporation Zhejiang Province Corporation, Hangzhou 310001, China
PRISMO: predictive skyline query processing over moving objects
Nan Chen, Li-dan Shou, Gang Chen, Yun-jun Gao, Jin-xiang Dong
School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; China National Tobacco Corporation Zhejiang Province Corporation, Hangzhou 310001, China
 全文: PDF 
摘要： Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branch-and-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPBBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.
关键词： Spatio-temporal database;  Moving object;  Skyline    
Abstract: Skyline query is important in the circumstances that require the support of decision making. The existing work on skyline queries is based mainly on the assumption that the datasets are static. Querying skylines over moving objects, however, is also important and requires more attention. In this paper, we propose a framework, namely PRISMO, for processing predictive skyline queries over moving objects that not only contain spatio-temporal information, but also include non-spatial dimensions, such as other dynamic and static attributes. We present two schemes, RBBS (branch-and-bound skyline with rescanning and repacking) and TPBBS (time-parameterized branch-and-bound skyline), each with two alternative methods, to handle predictive skyline computation. The basic TPBBS is further extended to TPBBSE (TPBBS with expansion) to enhance the performance of memory space consumption and CPU time. Our schemes are flexible and thus can process point, range, and subspace predictive skyline queries. Extensive experiments show that our proposed schemes can handle predictive skyline queries effectively, and that TPBBS significantly outperforms RBBS.
Key words: Spatio-temporal database    Moving object    Skyline
收稿日期: 2010-07-31 出版日期: 2012-01-19
CLC:  TP391.4  
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Nan Chen
Li-dan Shou
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引用本文:

Nan Chen, Li-dan Shou, Gang Chen, Yun-jun Gao, Jin-xiang Dong. PRISMO: predictive skyline query processing over moving objects. Front. Inform. Technol. Electron. Eng., 2012, 13(2): 99-117.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C10a0728    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I2/99

[1] Chang XU, Li-dan SHOU, Gang Chen, Yun-jun Gao. Index and retrieve the skyline based on dominance relationship[J]. Front. Inform. Technol. Electron. Eng., 2011, 12(1): 62-75.