Pub. Date:
Springer US
New Trends in Data Warehousing and Data Analysis / Edition 1

New Trends in Data Warehousing and Data Analysis / Edition 1

by Stanislaw Kozielski, Robert WrembelStanislaw Kozielski


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Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Today, knowledge-based management systems include data warehouses as their core components. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed to discover trends, patterns of behavior, and anomalies as well as finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data coming from many different sources make data integration and processing challenging.

New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies. It provides an up-to-date bibliography of published works and the resource of research achievements. Finally, the book assists in the dissemination of knowledge in the field of advanced DW and OLAP.

Product Details

ISBN-13: 9780387874302
Publisher: Springer US
Publication date: 11/21/2008
Series: Annals of Information Systems , #3
Pages: 364
Product dimensions: 6.10(w) x 9.20(h) x 0.80(d)

Table of Contents

Warehousing The World: A Vision for Data Warehouse Research.- Near Real Time ETL.- Data Warehousing Meets MDA.- Personalization and OLAP Databases.- A Metamodel for the Specification of Geographical Data.- Pushing Theoretically-Founded Probabilistic Guarantees in.- Closed Cube Lattices.- Design and Analysis of Index Structures in MultiVersion Data.- Using Bitmap Index for Joint Queries on Structured and Text Data.- HP&BJI: A Combined Selection of Data Partitioning and Join.- Cost Model for X-BR-tree.- Querying and Mining Trajectory Databases Using Places of Interest.- OLAP-Sequential Mining: Summarizing Trends from Historical.- Modeling and Querying Temporal Semistructured Data.- Designing and Implementing OLAP Systems from XML Documents.

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