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Data cube: A relational aggregation operator generalizing groupby, crosstab, and subtotals
, 1996
"... Abstract. Data analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zerodimensional or onedimensional aggregates. Applications need the Ndimensional generalization of these op ..."
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Abstract. Data analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zerodimensional or onedimensional aggregates. Applications need the Ndimensional generalization of these operators. This paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, crosstabulation, rollup, drilldown, and subtotal constructs found in most report writers. The novelty is that cubes are relations. Consequently, the cube operator can be imbedded in more complex nonprocedural data analysis programs. The cube operator treats each of the N aggregation attributes as a dimension of Nspace. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an Ndimensional cube. Superaggregates are computed by aggregating the Ncube to lower dimensional spaces. This paper (1) explains the cube and rollup operators, (2) shows how they fit in SQL, (3) explains how users can define new aggregate functions for cubes, and (4) discusses efficient techniques to compute the cube. Many of these features are being added to the SQL Standard.
H.Piramish, ”Data Cube: A Relational Aggregation Operator
 Generalizing, GroupBy, Crosstab, and SubTotals”, Int. Conference on Data Engineering
, 1996
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