Multi-table joins through bitmapped join indices
ACM SIGMOD Record
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Database (2nd ed.): principles, programming, and performance
Database (2nd ed.): principles, programming, and performance
ACM Computing Surveys (CSUR)
A performance comparison of bitmap indexes
Proceedings of the tenth international conference on Information and knowledge management
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
Strategies for processing ad hoc queries on large data warehouses
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
Model 204 Architecture and Performance
Proceedings of the 2nd International Workshop on High Performance Transaction Systems
Range-Based Bitmap Indexing for High Cardinality Attributes with Skew
COMPSAC '98 Proceedings of the 22nd International Computer Software and Applications Conference
Building the Data Warehouse
Optimizing bitmap indices with efficient compression
ACM Transactions on Database Systems (TODS)
Detecting distributed scans using high-performance query-driven visualization
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Bitmap Index Design Choices and Their Performance Implications
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
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Building indexes on database is common, but it has an important impact on the query performance, especially in large databases such as a Data Warehouse where the queries are usually very complex and ad hoc. If a proper index structure is chosen, the query response time can be accelerated. Until now, there is no definite guideline for Data Warehouse analysts to choose the appropriate index. According to conventional wisdom, Bitmap index is a preferred indexing technique for cases where the indexed attributes have few distinct values (i.e., low cardinality). The query response time is expected to degrade as the cardinality of indexed columns increase due to a larger index size. On the other hand, B-tree index is good if the column values are of high cardinality due to its indexing and retrieving mechanisms. In this paper, we show that this may not be true under certain circumstances. Experimental results support the fact that even though the level of column cardinality determines the index file size, but the query processing time is not determined by the level of column cardinality. Moreover, our results indicate that the Bitmap index is faster than B-tree index on a large dataset with multi-billion records.