SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Multidimensional access methods
ACM Computing Surveys (CSUR)
A greedy algorithm for bulk loading R-trees
Proceedings of the 6th ACM international symposium on Advances in geographic information systems
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
The Effect of Buffering on the Performance of R-Trees
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Multiple Range Query Optimization in Spatial Databases
ADBIS '98 Proceedings of the Second East European Symposium on Advances in Databases and Information Systems
Constrained Nearest Neighbor Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Group Nearest Neighbor Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
All-Nearest-Neighbors Queries in Spatial Databases
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Efficient Algorithm for Path-Based Range Query in Spatial Databases
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
Efficient processing of spatial joins with DOT-based indexing
Information Sciences: an International Journal
A new approach for similarity queries of biological sequences in databases
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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This paper revisits multi-point range query (MPRQ) for 2-d spatial database. In a previous paper, we introduced an efficient algorithm, PRQ, to answer the query for the case where database resides in main memory. This paper extends the algorithm to the general case in which the database is large and has to reside on disk. The MPRQ is defined as: Given a set of query points, P = {p1, p2, ..., pn}, and a search distance d, report all points in the spatial database that are within a distance d of some point pi in P. The simple method of performing Repeated Range Query (RRQ), i.e. the standard range query for each query point pi (1 ≤ i ≤ n) and combining the results is inefficient as it involves multiple searches on the database. We show that PRQ-Disk still achieve better results and outperform RRQ-Disk, as in the case of main memory. Extensive experiments using various real-life datasets, different R-tree variants (including bulk-loaded ones), over different query paths P, search distances d, and LRU buffering show that PRQ-Disk outperforms RRQ-Disk in terms of both query time and I/Os.