2002/12/23 by Jodi Gray, A. Bosworth, A. Lyaman +1 · 1 citation
Computer Science · Mathematics · Chemistry · #Data Management and Algorithms #Advanced Database Systems and Queries #Data Mining Algorithms and Applications #Cube (algebra) #Group (periodic table) #Operator (biology) #Relational database #Computer science #Data cube #Theoretical computer science #Mathematics #Data mining #Combinatorics #Physics #Chemistry
paper · doi:10.1109/icde.1996.492099
openalex publication_date 2002/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. The paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensionaI cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an "infinite value": ALL, so the point (ALL,ALL,...,ALL, sum(*)) represents the global sum of all items. Each ALL value actually represents the set of values contributing to that aggregation.