Bitmap index design and evaluation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An efficient bitmap encoding scheme for selection queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Space efficient bitmap indexing
Proceedings of the ninth international conference on Information and knowledge management
A performance comparison of bitmap indexes
Proceedings of the tenth international conference on Information and knowledge management
Performance Measurements of Compressed Bitmap Indices
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Optimizing Queries on Compressed Bitmaps
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Compressing Bitmap Indexes for Faster Search Operations
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
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
C-store: a column-oriented DBMS
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Bitmap Index Design Choices and Their Performance Implications
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
Compressing large boolean matrices using reordering techniques
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Static and incremental selection of multi-table indexes for very large join queries
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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Traditional bitmap indexes are utilized as a special type of primary or clustered indexes where the queries are answered by performing fast logical operations supported by hardware. Answers are mapped to the physical data by using the row id of each tuple. Bitmaps represent the i-th tuple in the original table with the i-th bit position of the index. Run-length compression is used to reduce the size of the bitmaps and it has been shown that ordered data is significantly better compressed. However, for large-scale and dynamic datasets it is infeasible to keep the data always sorted. Partitioning can be used to keep the data in smaller and manageable chunks, where a different bitmap index is built for each chunk. We propose a novel bitmap index design with partitioning which serves as basis for non-clustered bitmap indexes. Individual bitmaps are not stored, only an Existence Bitmap (EB) for the existing ranks of the full table is maintained. This approach improves update performance of sorted bitmaps and does not require maintaining a heap as the underlying table, nor the same ordering for all the partitions. A one dimensional index is used over the ranks to map the bits in the EB to the physical order of the data, which allows queries to run even faster. The proposed approach, called ranked Non-Clustered Bitmaps (rNCB), is compared against traditional bitmaps using FastBit and shows significant performance gains.