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Information 2012, 3(4), 661-675; doi:10.3390/info3040661
Article

Enhancing the Search in MOLAP Sparse Data

Received: 25 June 2012; in revised form: 23 September 2012 / Accepted: 8 November 2012 / Published: 14 November 2012
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Abstract: Multidimensional on-line analytical processing (MOLAP) systems deal well with dense data than relational ones (ROLAP). In the existence of sparse data, MOLAP systems become memory consuming, which may limit and slow down data processing tasks. Many compression techniques have been proposed to deal with the sparsity of data in MOLAP systems. One of these techniques is the bitmap compression, which allows a significant reduction of the memory space used for data processing. In this article, we propose an extension to the bitmap compression technique by storing the compressed data as bits into multiple efficient data structures based on a new indexing strategy instead of the linear structure. Compared with the classical bitmap, the proposed enhancement not only allows space reduction but also reduces the search time through the compressed data. We present some algorithms that allow maintaining and searching within the compressed structure without the need for decompression. We demonstrate that the complexity of the proposed algorithms varies from logarithmic to constant, compared with the linear complexity of the classical bitmap technique.
Keywords: data warehousing; MOLAP; bitmap compression; hashing data warehousing; MOLAP; bitmap compression; hashing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Zalaket, J. Enhancing the Search in MOLAP Sparse Data. Information 2012, 3, 661-675.

AMA Style

Zalaket J. Enhancing the Search in MOLAP Sparse Data. Information. 2012; 3(4):661-675.

Chicago/Turabian Style

Zalaket, Joseph. 2012. "Enhancing the Search in MOLAP Sparse Data." Information 3, no. 4: 661-675.


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