By Alejandro Vaisman, Visit Amazon's Esteban Zimányi Page, search results, Learn about Author Central, Esteban Zimányi,

With this textbook, Vaisman and Zimányi carry very good assurance of information warehousing and enterprise intelligence applied sciences starting from the main uncomplicated rules to fresh findings and functions. To this finish, their paintings is established into 3 components. half I describes “Fundamental suggestions” together with multi-dimensional types; conceptual and logical information warehouse layout and MDX and SQL/OLAP. for this reason, half II info “Implementation and Deployment,” together with actual facts warehouse layout; info extraction, transformation, and loading (ETL) and knowledge analytics. finally, half III covers “Advanced issues” equivalent to spatial information warehouses; trajectory facts warehouses; semantic applied sciences in info warehouses and novel applied sciences like Map lessen, column-store databases and in-memory databases.

As a key attribute of the booklet, many of the subject matters are awarded and illustrated utilizing software instruments. particularly, a case examine according to the well known Northwind database illustrates how the recommendations offered within the e-book might be applied utilizing Microsoft research providers and Pentaho enterprise Analytics. All chapters are summarized utilizing overview questions and workouts to aid entire pupil studying. Supplemental fabric to help teachers utilizing this e-book as a path textual content is on the market at http://cs.ulb.ac.be/DWSDIbook/, together with digital types of the figures, recommendations to all workouts, and a collection of slides accompanying each one chapter.

Overall, scholars, practitioners and researchers alike will locate this publication the main complete reference paintings on information warehouses, with key themes defined in a transparent and academic style.

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An important characteristic of topological relationships is that they do not change when the underlying space is distorted through rotation, scaling, and similar operations. Spatial databases offer sophisticated capabilities for the management of spatial data, including spatial index structures, storage management, and dynamic query formulation. However, similarly to conventional operational databases, they are typically targeted toward daily operations. Therefore, spatial databases are not well suited to support the decision-making process.

Give examples of applications for which such kinds of data are important. 14 Explain the differences between spatial databases and spatial data warehouses. 15 What is big data and how is it related to data warehousing? Give examples of technologies that are used in this context. 16 Describe why it is necessary to take into account web data in the context of data warehousing. Motivate your answer by elaborating an example application scenario. Chapter 2 Database Concepts This chapter introduces the basic database concepts, covering modeling, design, and implementation aspects.

11 Describe what is meant by the term data analytics. Give examples of techniques that are used for exploiting the content of data warehouses. 12 Why do we need a method for data warehouse design? 13 What is spatial data? What is spatiotemporal data? Give examples of applications for which such kinds of data are important. 14 Explain the differences between spatial databases and spatial data warehouses. 15 What is big data and how is it related to data warehousing? Give examples of technologies that are used in this context.

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