Advanced data warehouse design
From Conventional to Spatial and Temporal Applications
A data warehouse is essential for storing large volumes of historical data for analytical purposes, utilizing the extraction-transformation-loading (ETL) process to integrate data from operational databases into a coherent multidimensional model. Malinowski and Zimányi provide an in-depth exploration of conventional data warehouse design, focusing on complex hierarchy modeling. They also introduce innovative domains that enhance data warehouse systems, specifically the management of spatial and temporal information. The authors outline various phases of the design process, including requirements specification, and detail three approaches for gathering requirements based on user input or operational data sources, leading to the creation of a conceptual multidimensional model. The concepts are illustrated with real-world examples and sample implementations for Microsoft’s Analysis Services 2005 and Oracle 10g, including OLAP and Spatial extensions. This work serves as a comprehensive introduction to contemporary data warehouse design for researchers, with numerous references for further exploration. It also provides a clear foundation for graduate or advanced undergraduate courses, aiding experienced designers in expanding their analytical capabilities with spatial and temporal data. Additionally, experts in spatial databases or geographical information systems can leverage the data warehouse perspective for innovative spatial a
