Abstract
A bitmap-based index is an effective and efficient indexing method for answering selective queries in a read- only environment. It offers improved query execution time by applying low-cost Boolean operators on the index directly, before accessing raw data. A drawback of the bitmap index is that index size increases with the cardinality of indexed attributes, which additionally has an impact on a query execution time. This impact is related to an increase of query execution time due to the scanning of bitmap vectors to answer the queries. In this paper, we propose a new encoding bitmap index, called the HyBiX bitmap index. The HyBiX bitmap index was experimentally compared to existing encoding bitmap indexes in terms of space requirement, query execution time, and space and time trade-off for equality and range queries. As experimental results, the HyBiX bitmap index can reduce space requirements with high cardinality attributes with satisfactory execution times for both equality and range queries. The performance of the HyBiX bitmap index provides the second-best results for equality queries and the first-best for range queries in terms of space and time trade-off.
Highlights
The development of technologies has rapidly generated massive amounts of data from various sources [1,2,3], causing challenges in efficiently storing, searching, processing, analyzing, and managing such amounts of data.With increasing amounts of data, the efficiency of the query processing is a crucial issue when executing complex queries over large data by the traditional approaches [3, 4]
For the HyBiX bitmap index, the range query can be divided into two cases: either gv1 = gv2 or gv1 < gv2
The result confirms that the HyBiX bitmap index outperforms the other encoding bitmap indexes for the range queries regarding space and time trade-off with both attributes
Summary
The development of technologies has rapidly generated massive amounts of data from various sources [1,2,3], causing challenges in efficiently storing, searching, processing, analyzing, and managing such amounts of data. Several approaches have been demonstrated to speed up searching operations, such as parallel processing, materialized views, and indexing [4,5,6,7,8,9] Among these three, indexing is an efficient approach to retrieve data without requiring additional hardware and to enable answering ad hoc and complex queries in reasonable times [7, 10,11,12]. The basic bitmap index is easy to use for representing the data in binary format, with 0s and 1s, and allows fast query processing by low-cost Boolean operations (AND, OR, NOT, XOR) on the index directly before accessing the actual data. When dealing with high cardinality attributes, the basic, range, and interval bitmap indexes suffer from impractical storage requirements.
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