PRISMO: predictive skyline query processing over moving objects  

作　　者：Nan CHEN Li-dan SHOU Gang CHEN Yun-jun GAO Jin-xiang DONG 

机构地区：[1]School of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China [2]China National Tobacco Corporation Zhejiang Province Corporation,Hangzhou 310001,China

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2012年第2期99-117,共19页浙江大学学报C辑（计算机与电子（英文版）

基　　金：supported by the National Natural Science Foundation of China (Nos. 60603044 and 60803003);the Program for Changjiang Scholars and Innovative Research Team in University(No. IRT0652)

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 TPRBS 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.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 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP37[自动化与计算机技术—计算机科学与技术]