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Data Warehouses and Olap: Concepts, Architectures and Solutions

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Nowadays the economy is characterized by fast and continuously changing markets and business opportunities. Therefore, in order to be successful, it is essential for an enterprise to make right business decisions and to make them fast. Business decisions are taken on the basis of analyses of the past and current condition of an enterprise as well as market analysis and predictions for the future. To this end, various business operational data collected during the lifetime of an enterprise are analyzed. Typically, operational data are stored within an enterprise in multiple data storage systems (subsystems) that are geographically distributed, are heterogeneous and autonomous.

The heterogeneity of data storage systems means that they come from different software vendors; they are implemented in different technologies (e.g., C, C++, .Net, Java, 4th generation programming languages); they offer different functionality (e.g., fully-functional databases, ODBC data sources, spreadsheets, Web pages, text files); they use different data models (e.g., relational, object-relational, object-oriented, semistructured) and different storage techniques; they are installed on different operating systems and use different communication protocols.

The autonomy of data storage systems implies that they are often independent from each other and remain under separate, independent control; that is, a local system’s administrator can decide which local data are to be accessible from the outside of the system.

The management of an enterprise requires a comprehensive view of all aspects of a company, thus it requires access to all possible data of interest stored in multiple subsystems. However, an analysis of data stored in distributed, heterogeneous, and autonomous subsystems is likely to be difficult, slow, and inefficient. Therefore, the ability to integrate information from multiple data sources is crucial for today’s business.
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