Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications

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Springer Science & Business Media, Jan 22, 2008 - Computers - 435 pages

A data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process.

Malinowski and Zimányi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft's Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions.

For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers to enlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical applications.

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Selected pages


Introduction to Databases and Data Warehouses
Conventional Data Warehouses
Spatial Data Warehouses
Temporal Data Warehouses
Designing Conventional Data Warehouses
Designing Spatial and Temporal Data Warehouses
Conclusions and Future Work
Formalization of the MultiDim Model
Graphical Notation

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Page 393 - EF Codd, SB Codd, and CT Salley. Providing OLAP (On-line Analytical Processing) to user-analysts: An IT Mandate.
Page 367 - The map / is said to be continuous on X if it is continuous at every point of X.
Page 393 - J. Eder, C. Koncilia, and T. Morzy. A model for a temporal data warehouse. In Proceedings of the International Workshop on Open Enterprise Solutions: Systems, Experiences, and Organizations, pages 48-54, 2001.
Page 390 - Deriving initial data warehouse structures from the conceptual data models of the underlying operational information systems. In ACM Second international Workshop on Data Warehousing and OLAP (DOLAP), pages 15-21, Kansas City, Missouri, USA, November 1999.
Page 38 - This is the number of transactions that can be processed in a given time interval. In some systems, such as airline reservations, high transaction throughput is critical to the overall success of the system.
Page 393 - Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery, DaWaK'0l, LNCS 2114, pages 284-293.
Page 22 - A weak entity type normally has a partial key, which is the set of attributes that can uniquely identify weak entities that are related to the same owner...
Page 40 - With related rows being physically stored together, disk access time is improved. The related columns of the tables in a cluster are called the cluster key. The cluster key is stored only once, and so clusters store a set of tables more efficiently than if the tables were stored individually (not clustered).

About the author (2008)

Elzbieta Malinowski is a professor at the department of Computer and Information
Science at the Universidad de Costa Rica and a professional consultant in
Costa Rica in the area of the Data Warehousing. She received her master degrees
from Saint Petersburg Electrotechnical University, Russia (1982) and
University of Florida, USA (1996), and her Ph.D. degree from
Université Libre de Bruxelles, Belgium (2006). Her research interests
include data warehouses, OLAP systems, geographic information systems,
and temporal databases.

Esteban Zimányi is a professor of computer science at the Engineering Department of the Université Libre de Bruxelles (ULB), Belgium. He received the BSc degree (1988) and the doctorate degree (1992) in computer science from the Sciences Department at the ULB. His current research interests include conceptual modeling, geographic information systems, spatio-temporal databases, and semantic web.