Advanced Data Warehouse Design: From Conventional to Spatial by Elzbieta Malinowski

By Elzbieta Malinowski

This extraordinary paintings offers readers with an advent to the state of the art learn on information warehouse layout, with many references to extra designated assets. It deals a transparent and a concise presentation of the key recommendations and ends up in the topic quarter. Malinowski and Zim?nyi clarify traditional facts warehouse layout intimately, and also tackle cutting edge domain names lately brought to increase the features of information warehouse platforms: specifically, the administration of spatial and temporal info.

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Appendix B summarizes the notation used in this book for representing the constructs of the ER, relational, and object-relational models, as well as for conventional, spatial, and temporal data warehouses. 2 Introduction to Databases and Data Warehouses This chapter introduces the basic concepts of databases and data warehouses. It compares the two fields and stresses the differences and complementarities between them. The aim of this chapter is to define the terminology and the framework used in the rest of the book, not to provide an extensive coverage of these fields.

1, where an additional attribute Department id has been added with respect to the relation with the same name in Fig. 2. Suppose that members of the academic staff works in several different departments. Since the research areas of staff members are independent of the departments in which they work, there is a redundancy in the above table. Indeed, the information about the research areas of a staff member will be repeated as many times as the number of departments in which he/she works. Dependencies and normal forms are used to precisely describe the redundancies above.

This aspect is currently ignored in research work, which considers only valid time. As for conventional and spatial data warehouses, we propose a mapping of temporal data warehouses to the object-relational model. , [54, 273]), this mapping will help implementers who use the MultiDim model for designing temporal data warehouses. Finally, we propose a method for temporal-data-warehouse design that extends the method described for conventional-data-warehouse design. We refer to various cases, considering users’ requirements for temporal support and the availability of historical data in the source systems.

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