Storage Retrieval

Download Data Warehouse Systems: Design and Implementation by Alejandro Vaisman, Visit Amazon's Esteban Zimányi Page, PDF

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

With this textbook, Vaisman and Zimányi carry first-class insurance of information warehousing and company intelligence applied sciences starting from the main uncomplicated ideas to contemporary findings and functions. To this finish, their paintings is dependent into 3 elements. half I describes “Fundamental ideas” together with multi-dimensional types; conceptual and logical information warehouse layout and MDX and SQL/OLAP. thus, half II information “Implementation and Deployment,” which include actual information warehouse layout; facts extraction, transformation, and loading (ETL) and information analytics. finally, half III covers “Advanced issues” similar to spatial facts warehouses; trajectory information 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 ebook, many of the subject matters are offered and illustrated utilizing program instruments. in particular, a case research in keeping with the well known Northwind database illustrates how the strategies provided within the publication might be carried out utilizing Microsoft research companies and Pentaho company Analytics. All chapters are summarized utilizing evaluate questions and workouts to aid finished scholar studying. Supplemental fabric to help teachers utilizing this ebook as a path textual content is out there at, together with digital models of the figures, suggestions to all routines, and a collection of slides accompanying each one chapter.

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

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Example text

Dimensions typically include attributes that form hierarchies, which allow users to explore measures at various levels of detail. Examples of hierarchies are month–quarter–year in the time dimension and city–state–country in the location dimension. Aggregation of measures takes place when a hierarchy is traversed. For example, moving in a hierarchy from a month level to a year level will yield aggregated values of sales for the various years. From a methodological point of view, data warehouses must be designed analogously to operational databases, that is, following the four-step process consisting of requirements specification and conceptual, logical, and physical design.

The Northwind database does not include a generalization. To give an example, consider Fig. 3, in which we have a supertype, Employees, and two subtypes, PermanentEmployees and TemporaryEmployees. The former has an additional attribute Salary, and the latter has attributes ContractExpiration and ContractAmount. Generalization has three essential characteristics. The first one is population inclusion, meaning that every instance of the subtype is also an instance of the supertype. In our example, this means that every temporary employee is also an employee of the Northwind company.

The primary key of T is composed of all of its attributes. Suppose that in Fig. 1, the attribute Phone of Customers is multivalued. In this case, the attribute is mapped to a table CustomerPhone with attributes CustomerID and Phone both composing the primary key. Rule 7: A generalization relationship between a supertype E1 and subtype E2 can be dealt with in three different ways: Rule 7a: Both E1 and E2 are mapped, respectively, to tables T1 and T2 , in which case the identifier of E1 is propagated to T2 .

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