Efficient Query Execution over Large Databases through Semantic Caching of Bitmap Indices

As popular applications become increasingly data-intensive the need for novel techniques to query large-scale data stores becomes more prevalent. Because computers’ hard drives are slow, the more data is stored, the longer it takes to access useful information. This project investigates the use of semantic cache on bitmap indices. Semantic caching would allow us to split a query into two pieces, one referring to data contained already in the cache, and the other to data that needs to be fetched from main memory. This architecture should be able to produce the benefits of the regular caching policy, even when there are only partial results contained in the cache. This research investigates the use of semantic caching and its effect on performance speed for common database queries