Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results...Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named "compressed global tree guide" (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMinerTID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.展开更多
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 ...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.展开更多
基金the National Natural Science Foundation of China (No. 60603044)the National Key Technologies Supporting Program of China during the 11th Five-Year Plan Period (No. 2006BAH02A03)the Program for Changjiang Scholars and Innovative Research Team in University of China (No. IRT0652)
文摘Querying XML data is a computationally expensive process due to the complex nature of both the XML data and the XML queries. In this paper we propose an approach to expedite XML query processing by caching the results of frequent queries. We discover frequent query patterns from user-issued queries using an efficient bottom-up mining approach called VBUXMiner. VBUXMiner consists of two main steps. First, all queries are merged into a summary structure named "compressed global tree guide" (CGTG). Second, a bottom-up traversal scheme based on the CGTG is employed to generate frequent query patterns. We use the frequent query patterns in a cache mechanism to improve the XML query performance. Experimental results show that our proposed mining approach outperforms the previous mining algorithms for XML queries, such as XQPMinerTID and FastXMiner, and that by caching the results of frequent query patterns, XML query performance can be dramatically improved.
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)
文摘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.
