A Comparative Analysis of Traditional and Modern Data Compression Schemes for Large Multi-Dimensional Extendible Array
Data analysis and mining in scientific domains involve storage of large-scale multi-dimensional datasets for scientific, statistical & engineering applications in multidimensional online analytical processing (MOLAP) databases. Because of the size of the datasets is increasing and the degree of data-sparsity is being high, it is important to find the suitable and efficient compression scheme for storing data at a minimal scheme. This paper represents a comparative analysis of Traditional and Modern Data Compression Schemes for Multi-Dimensional data ranging from dimension 1 to 3. The main idea is to compare the space savings of four different & significant compressions schemes i.e. Bit Map, Header Compression, Compressed Row Storage (CRS) & Extendible Array Based Compression Scheme (EaCRS). The results from experiments show that EaCRS scheme is better than the other schemes in case of space complexity especially for higher data density.
Arrays, Data compression, Databases, Data centers, Distance measurement, Complexity theory, Performance evaluation