Economic incentives for database normalization
Information Processing and Management: an International Journal
Breaking all the rules: an insider's guide to practical normalization
Data Based Advisor
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ACM SIGMOD Record
Advanced database systems
Database (2nd ed.): principles, programming, and performance
Database (2nd ed.): principles, programming, and performance
The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling
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Rewriting OLAP Queries Using Materialized Views and Dimension Hierarchies in Data Warehouses
Proceedings of the 17th International Conference on Data Engineering
Denormalization guidelines for base and transaction tables
ACM SIGCSE Bulletin
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HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
An Introduction to Database Systems
An Introduction to Database Systems
Data Warehousing with SQL Server 2005
Data Warehousing with SQL Server 2005
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Mastering Data Warehouse Aggregates: Solutions for Star Schema Performance
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Denormalization strategies for data retrieval from data warehouses
Decision Support Systems
Bitmap Index Design Choices and Their Performance Implications
IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
ONE: a predictable and scalable DW model
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
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Proceedings of the 15th Symposium on International Database Engineering & Applications
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ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
Providing timely results with an elastic parallel DW
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
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Data normalization and denormalization processes are common in database design community as these processes have a great impact on the underlying performance. Current data warehouse queries involve a set of aggregations and joining operations. Thus, normalization process is not a good choice as many relations need to be merged in order to answer queries involving aggregation. On the other hand, denormalization process engages a lot of administrative task. This task takes into account the documentation structure of the denormalization assessments, data validation, schedule of migrating of data and so on. In this paper, we show that the mentioned justifications can not be convincible reasons, under certain circumstances, to ignore the effects of denormalization. Until now denormalization techniques have been introduced for various types of database design. One of the techniques is hierarchical denormalization. Our experimental results indicate that the query response time is significantly decreased when the schema is deployed by hierarchical denormalization on a large dataset with multibillion records. Thus, we suggest that hierarchical denormalization could be considered as a fundamental method to enhance query processing performance.