Text compression
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Compression of correlated bit-vectors
Information Systems
S-tree: a dynamic balanced signature index for office retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Managing gigabytes (2nd ed.): compressing and indexing documents and images
Managing gigabytes (2nd ed.): compressing and indexing documents and images
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Compressing Relations and Indexes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Exploiting clustering in inverted file Compression
DCC '96 Proceedings of the Conference on Data Compression
Independent Quantization: An Index Compression Technique for High-Dimensional Data Spaces
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Multidimensional Binary Search Trees in Database Applications
IEEE Transactions on Software Engineering
IBM Journal of Research and Development
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Tree-structured indexes typically restrict the search domain level by level, which means that the search information can be encoded more and more compactly on the way down. This simple observation is here formulated as a general principle of index compression. Saving storage space is one advantage, but more important is reduction of disk accesses, because more entries can be packed into a page. The index fan-out can be increased, reducing the average height of the tree. The applicability of compression is studied for several popular one-and multi-dimensional indexes. Experiments with the well-known spatial index, R*-tree, show that with modest assumptions and simple coding, 30-40% reduction of disk accesses is obtainable for intersection queries. Compression of index entries can be used together with other index compaction techniques, such as quantization and pointer list compression.