High Performance Queries Using Compressed Bitmap Indexes

Data that often contain unchanging records is becoming increasingly important. Many data sources, such as historical archives, sensor readings, health systems, and machine logs, do not change frequently but are constantly increasing. For this reason, the need to process such datasets more quickly has emerged. Bitmap index that can benefit from multicore and multiprocessor systems is designed to process data that has grown over time but does not change frequently. It has a well-known advantage, particularly in low cardinality data queries. Data such as gender, age, marital status, postal code and even date with low cardinality occupy an important place in datasets. Furthermore, the bitmap index using the compression algorithm can be applied efficiently even if the data has a high cardinality. In this study, bitmap index is introduced to improve queries and it has been shown to perform up to 20x faster queries with an appropriate encoding for data containing frequently unchanging records in a performance comparison against a commonly used relational database system.