PARSIMONY: An Infrastructure for Parallel Multidimensional Analysis and Data Mining

Multidimensional analysis and online analytical processing (OLAP) operations require summary information on multidimensional data sets. Most common are aggregate operations along one or more dimensions of numerical data values. Simultaneous calculation of multidimensional aggregates are provided by the Data Cube operator, used to calculate and store summary information on a number of dimensions. This is computed only partially if the number of dimensions is large. Query processing for these applications requires different views of data to gain insight and for effective decision support. Queries may either be answered from a materialized cube in the data cube or calculated on the fly. The multidimensionality of the underlying problem can be represented both in relational and in multidimensional databases, the latter being a better fit when query performance is the criteria for judgment. Relational databases are scalable in size for OLAP and multidimensional analysis and efforts are on to make their performance acceptable. On the other hand multidimensional databases have proven to provide good performance for such queries, although they are not very scalable. In this article we address (1) scalability in multidimensional systems for OLAP and multidimensional analysis and (2) integration of data mining with the OLAP framework. We describe our doi:10.1006 jpdc.2000.1691, available online at http: www.idealibrary.com on