simultaneous optimization and evaluation of multiple dimensional queries
Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little or no work on multidimensional query optimization. Recently, Microsoft has proposed OLE DB for OLAP as a standard multidimensional interface for databases. OLE DB for OLAP defines Multi-Dimensional Expressions (MDX), which have the interesting and challenging feature of allowing clients to ask several related dimensional queries in a single MDX expression. In this paper, we present three algorithms to optimize multiple related dimensional queries. Two of the algorithms focus on how to generate a global plan from several related local plans. The third algorithm focuses on generating a good global plan without first generating local plans. We also present three new query evaluation primitives that allow related query plans to share portions of their evaluation. Our initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.
optimality evaluation queries queries data database research data analysis analysis developer algorithm efficiency scheme design storage query optimization standardization interfaces express feature paper global plan locality query evaluation sharing performance exploit global optimization yield relational database systems group query