OLAP with a Database Cluster

OLAP with a Database Cluster

Uwe Rohm (University of Sydney, Australia)
Copyright: © 2007 |Pages: 23
DOI: 10.4018/987-1-59904-364-7.ch010
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Abstract

This chapter presents a new approach to on-line decision support systems that is scalable, fast, and capable of analysing even up-to-date data. It is based on a database cluster: a cluster of commercial off-the-shelf computers as hardware infrastructure and off-the-shelf database management systems as transactional storage managers. We focus on central architectural issues and on the performance implications of such a cluster-based decision support system. In the first half, we present a scalable infrastructure and discuss physical data design alternatives for cluster-based on-line decision support systems. In the second half of the chapter, we discuss query routing algorithms and freshness-aware scheduling. This protocol enables users to seamlessly decide how fresh the data analysed should be by allowing for different degrees of freshness of the OLAP nodes. In particular it becomes then possible to trade freshness of data for query performance.

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Table of Contents
Foreword
Tadeusz Morzy
Preface
Robert Wrembel, Christian Koncilia
Acknowledgments
Robert Wrembel, Christian Koncilia
Chapter 1
Stefano Rizzi
In the context of data warehouse design, a basic role is played by conceptual modeling, that provides a higher level of abstraction in describing... Sample PDF
Conceptual Modeling Solutions for the Data Warehouse
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Chapter 2
Carlos A. Hurtado, Claudio Gutierrez
Structural heterogeneous OLAP data arise when several OLAP dimensions with different structure are mixed into a single OLAP dimension. In this... Sample PDF
Handling Structural Heterogeneity in OLAP
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Chapter 3
Alejandro Vaisman
Today, information and timely decisions are crucial for an organization’s success. A Decision Support System is a software tool that provides... Sample PDF
Data Quality-Based Requirements Elicitation for Decision Support
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Chapter 4
Jovanka Adzic, Valter Fiore, Luisella Sisto
ETL stands for Extraction, Transformation, and Loading: in other words, for the Data Warehouse (DW) backstage. The main focus of our exposition here... Sample PDF
Extraction, Transformation, and Loading Processes
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Chapter 5
Alkis Simitsis, Panos Vassiliadis, Spiros Skiadopoulos, Timos Sellis
In the early stages of a data warehouse project, the designers/administrators have to come up with a decision concerning the design and deployment... Sample PDF
Data Warehouse Refreshment
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Chapter 6
Nikos Karayannidis, Aris Tsois, Timos Sellis
Star queries are the most prevalent kind of queries in data warehousing, OLAP and business intelligence applications. Thus, there is an imperative... Sample PDF
Advanced Ad Hoc Star Query Processing
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Chapter 7
Kurt Stockinger, Kesheng Wu
In this chapter we discuss various bitmap index technologies for efficient query processing in data warehousing applications. We review the existing... Sample PDF
Bitmap Indices for Data Warehouses
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Chapter 8
Karen C. Davis, Ashima Gupta
Bitmap Indexes (BIs) allow fast access to individual attribute values that are needed to answer a query by storing a bit for each distinct value and... Sample PDF
Indexing in Data Warehouses: Bitmaps and Beyond
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Chapter 9
Pedro Furtado
Running large data warehouses (DW) efficiently over low cost platforms places special requirements on the design of system architecture. The idea is... Sample PDF
Efficient and Robust Node-Partitioned Data Warehouses
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Chapter 10
Uwe Rohm
This chapter presents a new approach to on-line decision support systems that is scalable, fast, and capable of analysing even up-to-date data. It... Sample PDF
OLAP with a Database Cluster
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Chapter 11
Rokia Missaoui, Ganaël Jatteau, Ameur Boujenoui, Sami Naouali
In this paper, we present alternatives for coupling data warehousing and data mining techniques so that they can benefit from each other’s advances... Sample PDF
Toward Integrating Data Warehousing with Data Mining Techniques
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Chapter 12
Carlo Combi, Barbara Oliboni
This chapter describes a graph-based approach to represent information stored in a data warehouse, by means of a temporal semistructured data model.... Sample PDF
Temporal Semistructured Data Models and Data Warehouses
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Chapter 13
Yvan Bedard, Sonia Rivest, Marie-Josée Proulx
It is recognized that 80% of data have a spatial component (ex. street address, place name, geographic coordinates, map coordinates). Having the... Sample PDF
Spatial Online Analytical Processing (SOLAP): Concepts, Architectures, and Solutions from a Geomatics Engineering Perspective
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