Statistical relational tables for statistical database management
IEEE Transactions on Software Engineering
Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
The derivation problem of summary data
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Aggregate evaluability in statistical databases
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Real world requirements for decision support—implications for RDBMS
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A language and a physical organization technique for summary tables
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On the Data Model and Access Method of Summary Data Management
IEEE Transactions on Knowledge and Data Engineering
Mefisto: A Functional Model for Statistical Entities
IEEE Transactions on Knowledge and Data Engineering
Physical Schemas for Large Multidimensional Arrays in Scientific Computing Applications
Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management
A Physical Storage for Efficient Statistical Query Processing
Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management
Information systems research at George Mason University
ACM SIGMOD Record
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A statistical query first manipulates source category data to build a target category in the form of a grouping relation and then performs statistical functions on the associated measurement data. In this paper, the attributes in a grouping relation are partitioned into pair-wise disjoint sets, each called a dimension. A grouping relation is said to be orthogonal if it is equal to the cross product of the projections of itself on all the dimensions. Orthogonality is useful in searching for and using pre-computed summaries on other categories. However, a grouping relation is sometimes not orthogonal, but rather k-partially orthogonal (i.e., the union of k orthogonal ones). It is shown that it is NP-complete to decide if a grouping relation is k-partially orthogonal. The paper then gives an algorithm to derive partial orthogonality. Also investigated in this paper are interval properties of grouping relations useful for optimizing statistical queries. An algorithm is described to derive interval properties.