A dimensional modeling manifesto
DBMS - Special issue on data warehousing
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
A multidimensional and multiversion structure for OLAP applications
Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
What can Hierarchies do for Data Warehouses?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Why is the snowflake schema a good data warehouse design?
Information Systems
Improving OLAP Performance by Multidimensional Hierarchical Clustering
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Reconsidering Multi-Dimensional schemas
ACM SIGMOD Record
Triple-driven data modeling methodology in data warehousing: a case study
DOLAP '06 Proceedings of the 9th 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
Processing star queries on hierarchically-clustered fact tables
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
SAMSTAR: An Automatic Tool for Generating Star Schemas from an Entity-Relationship Diagram
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Data Warehouse Design: Modern Principles and Methodologies
Data Warehouse Design: Modern Principles and Methodologies
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A fundamental issue encountered by the research community of data warehouses DWs is the modeling of data. In this paper, a new design is proposed, named the starnest schema, for the logical modeling of DWs. Using nested methodology, data semantics can be explicitly represented. Part of the design involves providing a translation mechanism from the star/snowflake schemas to a nested representation. The novel schema proposed in this paper is accomplished by converting the fact-dimension schema to a fact-nested dimension schema. The transformation of the denormalized dimension tables to nested dimension tables increases the efficiency of query execution by reducing the number of tuples accessed for query retrieval since dimensional attributes can be used directly in the Group-by clause. In order to facilitate the implementation of the proposed approach, specific algorithms are built based on the starnest schema.