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In-Memory Data Management.

Author: Hasso Plattner, Alexander Zeier.
Publisher: Springer-Verlag Berlin Heidelberg 2011.
Edition/Format:   eBook : Document : English
Database:WorldCat
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Genre/Form: Electronic resource
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Hasso Plattner, Alexander Zeier.
ISBN: 128308029X 9781283080293
OCLC Number: 743409542
Description: 1 online resource
Contents: Cover13; --
In Praise of In-Memory Data Management: An Inflection Point for Enterprise Applications --
Academia --
Industry --
Foreword --
Preface --
Contents --
Introduction --
PART I 8211; An Inflection Point for Enterprise Applications --
1 Desirability, Feasibility, Viability 8211; The Impact of In-Memory --
1.1 Information in Real Time 8211; Anything, Anytime, Anywhere --
1.1.1 Response Time at the Speed of Thought --
1.1.2 Real-Time Analytics and Computation on the Fly --
1.2 The Impact of Recent Hardware Trends --
1.2.1 Database Management Systems for Enterprise Applications --
1.2.2 Main Memory Is the New Disk --
1.2.3 From Maximizing CPU Speed to Multi-Core Processors --
1.2.4 Increased Bandwidth between CPU and Main Memory --
1.3 Reducing Cost through In-Memory Data Management --
1.3.1 Total Cost of Ownership --
1.3.2 Cost Factors in Enterprise Systems --
1.3.3 In-Memory Performance Boosts Cost Reduction --
1.4 Conclusion --
2 Why Are Enterprise Applications So Diverse? --
2.1 Current Enterprise Applications --
2.2 Examples of Enterprise Applications --
Demand Planning --
Sales Order Processing --
Available-to-Promise --
Dunning --
Sales Analysis --
2.3 Enterprise Application Architecture --
2.4 Data Processing in Enterprise Applications --
2.5 Data Access Patterns in Enterprise Applications --
2.6 Conclusion --
3 SanssouciDB 8211; Blueprint for an In-Memory Enterprise Database System --
3.1 Targeting Multi-Core and Main Memory --
3.2 Designing an In-Memory Database System --
3.3 Organizing and Accessing Data in Main Memory --
3.4 Conclusion --
PART II 8211; SanssouciDB 8211; A Single Source of Truth through In-Memory --
4 The Technical Foundations of SanssouciDB --
4.1 Understanding Memory Hierarchies --
4.1.1 Introduction to Main Memory --
4.1.2 Organization of the Memory Hierarchy --
4.1.3 Trends in Memory Hierarchies --
4.1.4 Memory from a Programmers Point of View --
4.2 Parallel Data Processing Using Multi-Core and Across Servers --
4.2.1 Increasing Capacity by Adding Resources --
4.2.2 Parallel System Architectures --
4.2.3 Parallelization in Databases for Enterprise Applications --
4.2.4 Parallel Data Processing in SanssouciDB --
4.3 Compression for Speed and Memory Consumption --
4.3.1 Light-Weight Compression --
4.3.2 Heavy-Weight Compression --
4.3.3 Data-Dependent Optimization --
4.3.4 Compression-Aware Query Execution --
4.3.5 Compression Analysis on Real Data --
4.4 Column, Row, Hybrid 8211; Optimizing the Data Layout --
4.4.1 Vertical Partitioning --
4.4.2 Finding the Best Layout --
4.4.3 Challenges for Hybrid Databases --
4.5 The Impact of Virtualization --
4.5.1 Virtualizing Analytical Workloads --
4.5.2 Data Model and Benchmarking Environment --
4.5.3 Virtual versus Native Execution --
4.5.4 Response Time Degradation with Concurrent VM s --
4.6 Conclusion --
5 Organizing and Accessing Data in SanssouciDB --
5.1 SQL for Accessing In-Memory Data --
5.1.1 The Role of SQL --
5.1.2 The Lifecycle of a Query --
5.1.3 Stored Procedures --
5.1.4 Data Organization and Indices --
5.2 Increasing Performance with Data Aging --
5.2.1 Active and Passive Data --
5.2.2 Implementation Considerations for an Aging Process --
5.2.3 The Use Case for Horizontal Partitioning of Leads --
5.3 Efficient Retrieval of Business Objects --
5.3.1 Retrieving Business Data from.

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