By Alejandro Vaisman, Visit Amazon's Esteban Zimányi Page, search results, Learn about Author Central, Esteban Zimányi,
With this textbook, Vaisman and Zimányi convey very good assurance of information warehousing and company intelligence applied sciences starting from the main easy rules to contemporary findings and functions. To this finish, their paintings is established into 3 elements. half I describes “Fundamental innovations” together with multi-dimensional versions; conceptual and logical facts warehouse layout and MDX and SQL/OLAP. accordingly, half II information “Implementation and Deployment,” together with actual information warehouse layout; information extraction, transformation, and loading (ETL) and information analytics. finally, half III covers “Advanced themes” akin to spatial info warehouses; trajectory information warehouses; semantic applied sciences in facts warehouses and novel applied sciences like Map lessen, column-store databases and in-memory databases.
As a key attribute of the booklet, lots of the issues are offered and illustrated utilizing software instruments. particularly, a case examine in keeping with the well known Northwind database illustrates how the options offered within the ebook might be carried out utilizing Microsoft research providers and Pentaho enterprise Analytics. All chapters are summarized utilizing overview questions and routines to help accomplished scholar studying. Supplemental fabric to aid teachers utilizing this ebook as a path textual content is obtainable at http://cs.ulb.ac.be/DWSDIbook/, together with digital types of the figures, options to all routines, and a collection of slides accompanying each one chapter.
Overall, scholars, practitioners and researchers alike will locate this e-book the main entire reference paintings on information warehouses, with key issues defined in a transparent and academic style.
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Additional info for Data Warehouse Systems: Design and Implementation
1 shows the ER model for the Northwind database. We next introduce the main ER concepts using this ﬁgure. Entity types are used to represent a set of real-world objects of interest to an application. In Fig. 1, Employees, Orders, and Customers are examples of entity types. An object belonging to an entity type is called an entity or an instance. The set of instances of an entity type is called its population. From the application point of view, all entities of an entity type have the same characteristics.
Bottom-up design: A separate schema is built for each group of users with diﬀerent requirements, and later, during the view integration phase, these schemas are merged to form a global conceptual schema for the entire database. This is the approach typically used for large databases. • Logical design aims at translating the conceptual representation of the database obtained in the previous phase into a particular logical model common to several DBMSs. Currently, the most common logical model is the relational model.
For each one of them, the company name and phone number must be kept. Supplier data, including the company name, contact name and title, full address, phone, fax, and home page. Data about the products that Northwind trades, such as identiﬁer, name, quantity per unit, unit price, and an indication if the product has been discontinued. , the number of units in stock such that when it is reached, the company must produce or acquire). Products are further classiﬁed into categories, each of which has a name, a description, and a picture.