Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bitmap index design and evaluation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Query processing and optimization in Oracle Rdb
The VLDB Journal — The International Journal on Very Large Data Bases
Byte-aligned bitmap compression
DCC '95 Proceedings of the Conference on Data Compression
Data Compression
Compressing Bitmap Indices by Data Reorganization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Using bitmap index for interactive exploration of large datasets
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Compressing large boolean matrices using reordering techniques
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Variable length compression for bitmap indices
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Minimizing index size by reordering rows and columns
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Dynamic bitmap index recompression through workload-based optimizations
Proceedings of the 17th International Database Engineering & Applications Symposium
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Data reordering techniques are applied to improve the space and time efficiency of storage and query systems in various scientific and commercial applications. Run-length encoding is a prominent approach of compression in many areas, whose performance is significantly enhanced by achieving longer and fewer "runs" through data reordering. In this paper we theoretically study two reordering techniques, namely lexicographical order and Gray code order. We analyze these two methods in the context of bitmap indexes, which are known to have high query performances. We take into account the two commonly used bitmap encodings: equality and range. Our analysis indicates that, when we have all the possible data tuples, both ordering methods perform the same with equality encoding. However, Gray code achieves better compression with range encoding. Experimental results are provided to validate the theoretical analysis.