Storage Retrieval

Download Fundamentals of Data Warehouses by Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, Panos PDF

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By Matthias Jarke, Maurizio Lenzerini, Yannis Vassiliou, Panos Vassiliadis

Data warehouses have captured the eye of practitioners and researchers alike. however the layout and optimization of knowledge warehouses continues to be an artwork instead of a science.
This ebook provides the 1st comparative assessment of the state-of-the-art and top present perform of knowledge warehouses. It covers resource and knowledge integration, multidimensional aggregation, question optimization, replace propagation, metadata administration, caliber evaluation, and layout optimization. in addition, it provides a conceptual framework in which the structure and caliber of information warehouse efforts will be assessed and greater utilizing enriched metadata administration mixed with complex concepts from databases, enterprise modeling, and synthetic intelligence. For researchers and database pros in academia and undefined, the publication deals a very good creation to the problems of caliber and metadata utilization within the context of knowledge warehouses.
within the moment version, the numerous advances of the country of the perform within the final 3 years are mentioned and the conceptual framework is prolonged to a whole technique for facts warehouse layout, illustrated through a number of business case studies.

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To explain, consider Fig. 3 where the traditional flow of information is stylized on the right-hand side, whereas the process of creating and using the information is shown on the left. Suppose an analyst wants to know something about the business - the question mark in Fig. 3. The analyst does not have the time to observe the business directly but must rely on existing information gained by operational departments, and documented as a side effect of OLTP systems. This way of gathering information implies already a bias which needs to be compensated when selecting OLTP data for uploading and cleaning into a DW where it is then further preprocessed and aggregated in data marts for certain analysis tasks.

We sketch how such a methodological framework supports source integration in data warehousing. We refer the reader to [CDL*97, CDL*98b, CDL*98a, CDL*Ol] for a detailed treatment. 3 Towards Systematic Methodologies for Source Integration 41 Interface [;;8----.. 11 - '::::t ~-l ..... now I ...... (Sc~=-I }-.... \. '. ~ ................... =:. m l~ ----, "\ I . · ~ I···. . al mapping .......... J .. [ow~J Fig. 1. 1 Architecture for Source Integration The general architecture for source integration in data warehousing is depicted in Fig.

7 Three Perspectives of Data Warehouse Metadata Analyst ( OLAP Multidim. Data Mart (5) ? Aggregation! Y.. ___ _ Enterprise (1) Operational Department 23 -----+) (2) OLTP 'U+ Data Warehouse Wrapper/ Loader Information Source Fig. 3. Data warehousing in the context of an enterprise Indeed, Fig. 2 only covers two of the five steps shown in Fig. 3. " (could simply be a problem of wrong aggregation in the materialized views, could require access to not-yet-integrated OLTP sources, or could even involve setting up new OLTP sensors in the organization).

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