Modeling multidimensional databases

The authors propose a data model and a few algebraic operations that provide semantic foundation to multidimensional databases. The distinguishing feature of the proposed model is the symmetric treatment not only of all dimensions but also measures. The model provides support for multiple hierarchies along each dimension and support for ad hoc aggregates. The proposed operators are composable, reorderable, and closed in application. These operators are also minimal in the sense that none can be expressed in terms of others nor can any one be dropped without sacrificing functionality. They make possible the declarative specification and optimization of multidimensional database queries that are currently specified operationally. The operators have been designed to be translated to SQL and can be implemented either on top of a relational database system or within a special purpose multidimensional database engine. In effect, they provide an algebraic application programming interface (API) that allows the separation of the front end from the back end. Finally, the proposed model provides a framework in which to study multidimensional databases and opens several new research problems.

[1]  Zbigniew Michalewicz.  Statistical and Scientific Databases , 1991 .

[2]  Jeffrey F. Naughton,et al.  On the Computation of Multidimensional Aggregates , 1996, VLDB.

[3]  Jeffrey D. Ullman,et al.  Index selection for OLAP , 1997, Proceedings 13th International Conference on Data Engineering.

[4]  Arie Shoshani,et al.  On the Semantic Completeness of Macro-Data Operators for Statistical Aggregation , 1992, SSDBM.

[5]  Lotfi Lakhal,et al.  Matrix-Relation for Statistical Database Management , 1994, EDBT.

[6]  D. Shasha,et al.  Hierarchically Split Cube Forests for Decision Support: description and tuned design , 1996 .

[7]  Arie Shoshani,et al.  Statistical Databases: Characteristics, Problems, and some Solutions , 1982, VLDB.

[8]  Alfred V. Aho,et al.  Universality of data retrieval languages , 1979, POPL.

[9]  Venky Harinarayan,et al.  Implementing Data Cubes E ciently , 1996 .

[10]  Timos K. Sellis,et al.  On the Multiple-Query Optimization Problem , 1990, IEEE Trans. Knowl. Data Eng..

[11]  Jeffrey D. Ullman,et al.  Implementing data cubes efficiently , 1996, SIGMOD '96.

[12]  David Harel,et al.  Horn clauses and the fixpoint query hierarchy , 1982, PODS.

[13]  Laks V. S. Lakshmanan,et al.  Tables as a paradigm for querying and restructuring (extended abstract) , 1996, PODS '96.

[14]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[15]  O. W. Thomas.  Florida , 1980, Bird Student.

[16]  L. Nelson.  Data, data everywhere. , 1997, Critical Care Medicine.

[17]  Jeffrey F. Naughton,et al.  Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies , 1996, VLDB.

[18]  Donald D. Chamberlin,et al.  Using the New DB2: IBM's Object-Relational Database System , 1996 .

[19]  Sushil Jajodia,et al.  Temporal Databases: Theory, Design, and Implementation , 1993 .

[20]  Jaideep Srivastava,et al.  TBSAM: An Access Method for Efficient Processing of Statistical Queries , 1989, IEEE Trans. Knowl. Data Eng..

[21]  E. F. Codd,et al.  A relational model of data for large shared data banks , 1970, CACM.

[22]  E. F. Codd,et al.  A Relational Model for Large Shared Data Banks , 1970 .

[23]  George Colliat,et al.  OLAP, relational, and multidimensional database systems , 1996, SGMD.

[24]  A. Szykier.  Fractal compression of data structures , 1994, Proceedings of ELECTRO '94.

[25]  J. Meigs,et al.  WHO Technical Report , 1954, The Yale Journal of Biology and Medicine.

[26]  J.P. Stamen.  Structuring databases for analysis , 1993, IEEE Spectrum.