Compression Schemes of High Dimensional Data for MOLAP

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Author(s): K. M. Azharul Hasan (Khulna University of Engineering and Technology (KUET), Bangladesh)
Copyright: 2010
Pages: 18
Source title: Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions
Source Author(s)/Editor(s): Pedro Nuno San-Banto Furtado (University of Coimbra, Portugal)
DOI: 10.4018/978-1-60566-816-1.ch004

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

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Abstract

The exploration of the possibility of compressing data warehouses is inevitable because of their non-trivial storage and access costs. A typical large data warehouse needs hundreds of gigabytes to a terabyte of storage. Performance of computing aggregate queries is a bottleneck for many Online Analytical Processing (OLAP) applications. Hence, data warehousing implementations strongly depend on data compression techniques to make possible the management and storage of such large databases. The efficiency of data compression methods has a significant impact on the overall performance of these implementations. The purpose of this chapter is to discuss the importance of data compression to Multidimensional Online Analytical Processing (MOLAP), to survey data compression techniques relevant to MOLAP, and to discuss important quality issues of MOLAP compression and of existing techniques. Finally, we also discuss future research trends on this subject.

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