Review on High Dimensional Data Visualization (original) (raw)
2014, R S. Publication (rspublication.com),
Large high dimensional dataset encloses billions of entries and contains different attributes and relational databases. Data cube aggregation operation is a well-known technique used to implement data-mining for larger size databases. Spatial data are sometimes assumed to have absorbed prohibitively large amount of space, which consequently requires disk storage. Thus it is required to pre-compute every possible aggregate query over the database. Modern scientific applications are generating larger and larger volume of data at ever increasing rate. As datasets become bulkier, exploratory data visualization turns out to be more difficult and complex, and data-fetching turns into a time-consuming process in small devices. Nanocubes are in-memory data structures, specifically designed to speed up queries for multidimensional data cubes, and could eventually be used as a backend for these types of applications. Nanocubes offer efficient storage and querying of large, multidimensional, spatiotemporal datasets and high dimensional datasets.
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