Optimizing multidimensional index trees for main memory access
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Main-memory index structures with fixed-size partial keys
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Compacting discriminator information for spatial trees
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
Fractal prefetching B+-Trees: optimizing both cache and disk performance
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Database Architecture Optimized for the New Bottleneck: Memory Access
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
Buffering accesses to memory-resident index structures
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
TMOM: a moving object main memory-based DBMS for telematics services
W2GIS'06 Proceedings of the 6th international conference on Web and Wireless Geographical Information Systems
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Recently, in order to retrieve data objects efficiently according to spatial locations in the spatial main memory DBMS, various multi-dimensional index structures for the main memory have been proposed, which minimize failures in cache access by reducing the entry size. However, because the reduction of entry size requires compression based on the MBR (Minimum Bounding Rectangle) of the parent node or the removal of redundant MBR, the cost of MBR reconstruction increases and the efficiency of search is lowered in index update and search. Thus, to reduce the cost of MBR reconstruction, this paper proposed a RSMBR (Relative-Sized MBR) compression technique, which applies the base point of compression differently in case of broad distribution and narrow distribution. In case of broad distribution, compression is made based on the left-bottom point of the extended MBR of the parent node, and in case of narrow distribution, the whole MBR is divided into cells of the same size and compression is made based on the left-bottom point of each cell. In addition, MBR was compressed using a relative coordinate and the MBR size to reduce the cost of search in index search. Lastly, we evaluated the performance of the proposed RSMBR compression technique using real data, and proved its superiority.