YAM2: a multidimensional conceptual model extending UML
Information Systems
A data warehouse environment for storing and analyzing simulation output data
WSC '04 Proceedings of the 36th conference on Winter simulation
Denormalization strategies for data retrieval from data warehouses
Decision Support Systems
View Discovery in OLAP Databases through Statistical Combinatorial Optimization
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Parallel computing for data reduction
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
A parallel algorithm to compute data synopsis
WSEAS Transactions on Information Science and Applications
REQUEST: A Query Language for Customizing Recommendations
Information Systems Research
On the need of a reference algebra for OLAP
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse, or decision support database, can be defined as a "very large" repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, currently there is no standard conceptual model for OLAP. There clearly is a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.