Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Improved query performance with variant indexes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
DynaMat: a dynamic view management system for data warehouses
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Optimizing Queries with Aggregate Views
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Performing Group-By before Join
Proceedings of the Tenth International Conference on Data Engineering
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
Back to the Future: Dynamic Hierarchical Clustering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Integrating the UB-Tree into a Database System Kernel
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Including Group-By in Query Optimization
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Eager Aggregation and Lazy Aggregation
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Answering Queries with Aggregation Using Views
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The Universal B-Tree for Multidimensional Indexing: general Concepts
WWCA '97 Proceedings of the International Conference on Worldwide Computing and Its Applications
Answering Multidimensional Queries on Cubes Using Other Cubes
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Processing Operations with Restrictions in RDBMS without External Sorting: The Tetris Algorithm
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Improving OLAP Performance by Multidimensional Hierarchical Clustering
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Processing OLAP queries in hierarchically clustered databases
Data & Knowledge Engineering - Special issue: Advances in OLAP
Exploiting hierarchical clustering in evaluating multidimensional aggregation queries
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Hierarchies in a multidimensional model: from conceptual modeling to logical representation
Data & Knowledge Engineering - Special issue: WIDM 2004
LinearDB: a relational approach to make data warehouse scale like MapReduce
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
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On-line analytical processing (OLAP) is a technology that encompasses applications requiring a multidimensional and hierarchical view of data. OLAP applications often require fast response time to complex grouping/aggregation queries on enormous quantities of data. Commercial relational database management systems use mainly multiple one-dimensional indexes to process OLAP queries that restrict multiple dimensions. However, in many cases, multidimensional access methods outperform one-dimensional indexing methods.We present an architecture for multidimensional databases that are clustered with respect to multiple hierarchical dimensions. It is based on the star schema and is called CSB star. Then, we focus on heuristically optimizing OLAP queries over this schema using multidimensional access methods. Users can still formulate their queries over a traditional star scheme, which are then rewritten by the query processor over the CSB star. We exploit the different clustering features of the CSB star to efficiently process a class of typical OLAP queries. We detect special cases where the construction of an evaluation plan can be simplified and we discuss improvements of our technique.