OLAP Cube
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An OLAP Cube is a Data Structure that supports efficient Aggregation Functions.
- AKA: Cube, Data Cube, Hypercube, Multi-Dimensional Array, Multi-Dimensional Database.
- Context:
- It can be used by an OLAP System.
- It can (typically) be a Multi-dimensional Data Structure.
- See: Array, Sparse, Dense, Dimension Member, Multidimensional Query Language.
References
2009
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/OLAP_cube
- An OLAP (Online analytical processing) cube is a data structure that allows fast analysis of data. The arrangement of data into cubes overcomes a limitation of relational databases. Relational databases are not well suited for near instantaneous analysis and display of large amounts of data. Instead, they are better suited for creating records from a series of transactions known as OLTP or On-Line Transaction Processing.
1999
- (Zaiane, 1999) ⇒ Osmar Zaiane. (1999). “Glossary of Data Mining Terms." University of Alberta, Computing Science CMPUT-690: Principles of Knowledge Discovery in Databases.
- QUOTE: Data Cube: Also Cube, Hypercube, Multi-dimentional Array, Multi-dimentional Database. It is a multi-dimentional data structure, a group of data cells arranged by the dimensions of the data. For example, a spreadsheet exemplifies a two-dimensional array with the data cells arranged in rows and columns, each being a dimension. A three-dimensional array can be visualized as a cube with each dimension forming a side of the cube, including any slice parallel with that side. Higher dimensional arrays have no physical metaphor, but they organize the data in the way users think of their enterprise. Typical enterprise dimensions are time, measures, products, geographical location, sales channels, etc. It is not rare to see more than 20 dimensions. However, the higher the dimensions the more complex the manipulation and data mining on the cube become, and the more sparce the data cube may become.
- QUOTE: Cell: A single datapoint that occurs at the intersection defined by selecting one member from each dimension in a multi-dimensional array. For example, if the dimensions are measures, time, product and geography, then the dimension members: Sales, January 1996, Bicycle and Canada specify a precise intersection along all dimensions that uniquely identifies a single data cell, which contains the value of bicycle sales in Canada for the month of January 1996.
1993
- E. F. Codd, S. B. Codd, and C. T. Salley. (1993). “Providing OLAP (On-line Analytical Processing) to User-Analysts: An IT Mandate. Codd & Date, Inc.