A General Analytical Model for Spatial and Temporal Performance of Bitmap Index Compression Algorithms in Big Data

Wu, Yinjun; Chen, Zhe; Wen, Yang; Cao, Junwei; Zheng, Wenxun; Ma, Ge

doi:10.1109/icccn.2015.7288362

Cited by 6 publications

(6 citation statements)

References 15 publications

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“…[30] does not provide the theoretical compression results of COMPAX, they should be the same as the compression results of SECOMPAX in Ref. [30], because the two algorithms share the same compression schemes, in the case of sparse bitmaps. So the corresponding result of COMPAX is…”

Section: Sparse Bitmapmentioning

confidence: 84%

“…But here, we denote these values using r, which is realized by replacing d in the probability value from Ref. [30] with 1 r. These new probability values and their simplified values are presented in Table 1.…”

Section: Dense Bitmapmentioning

confidence: 98%

“…Similar to Ref. [30], We list some assumptions below that simplify our analysis. The following analysis is composed of three different possible cases, i.e., sparse bitmaps, dense bitmaps, and bitmaps following a Zipf distribution.…”

Section: Theoretical Analysesmentioning

confidence: 98%

“…[30], the theoretical compression results of CONCISE and COMBAT (denoted by L CONCISE and L COMBAT ) are shown below:…”

Section: Sparse Bitmapmentioning

confidence: 99%

“…[30], the basic probabilities, i.e., the probabilities of fill, L, NI2-L, literal, and N-fill in an uncompressed bit sequence, can be calculated. But here, we denote these values using r, which is realized by replacing d in the probability value from Ref.…”

Section: Dense Bitmapmentioning

confidence: 99%

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COMBAT: a new bitmap index coding algorithm for big data

Chen

Wen

et al. 2016

Tinshhua Sci. Technol.

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Bitmap indexing has been widely used in various applications due to its speed in bitwise operations. However, it can consume large amounts of memory. To solve this problem, various bitmap coding algorithms have been proposed. In this paper, we present COMbining Binary And Ternary encoding (COMBAT), a new bitmap index coding algorithm. Typical algorithms derived from Word Aligned Hybrid (WAH) are COMPressed Adaptive indeX (COMPAX) and Compressed "n" Composable Integer Set (CONCISE), which can combine either two or three continuous words after WAH encoding. COMBAT combines both mechanisms and results in more compact bitmap indexes. Moreover, querying time of COMBAT can be faster than that of COMPAX and CONCISE, since bitmap indexes are smaller and it would take less time to load them into memory. To prove the advantages of COMBAT, we extend a theoretical analysis model proposed by our group, which is composed of the analysis of various possible bitmap indexes. Some experimental results based on real data are also provided, which show COMBAT's storage and speed superiority. Our results demonstrate the advantages of COMBAT and codeword statistics are provided to solidify the proof.

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Section: Sparse Bitmapmentioning

confidence: 84%

Section: Dense Bitmapmentioning

confidence: 98%

Section: Theoretical Analysesmentioning

confidence: 98%

“…[30], the theoretical compression results of CONCISE and COMBAT (denoted by L CONCISE and L COMBAT ) are shown below:…”

Section: Sparse Bitmapmentioning

confidence: 99%

Section: Dense Bitmapmentioning

confidence: 99%