Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Spatial hierarchy and OLAP-favored search in spatial data warehouse
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Information integration: A research agenda
IBM Systems Journal
GPIVOT: Efficient Incremental Maintenance of Complex ROLAP Views
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
An intelligent XML-based multidimensional data cube exchange
Expert Systems with Applications: An International Journal
Hybrid index for spatio-temporal OLAP operations
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
Extension of r-tree for spatio-temporal OLAP operations
ICDCIT'06 Proceedings of the Third international conference on Distributed Computing and Internet Technology
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Enterprises have been storing multidimensional data, using a star or snowflake schema, in relational databases for many years. Over time, relational database vendors have added optimizations that enhance query performance on these schemas. During the 1990s many special-purpose databases were developed that could handle added calculational complexity and that generally performed better than relational engines. DB2脗® has added a number of features that make it more competitive with these special-purpose databases. In this paper, we define meta-data extensions that allow designers of multidimensional schemas to describe the structure of those schemas to multidimensional query and analysis tools. The SQL (Structured Query Language) extensions include a "cube" object that returns row sets that are "slices" of the cube. We also describe Web services for OLAP (on-line analytical processing) that provide meta-data for multidimensional data, as well as XML (Extensible Markup Language) query results.