Blazewicz / Kubiak / Morzy | Handbook on Data Management in Information Systems | E-Book | sack.de
E-Book

E-Book, Englisch, 578 Seiten, eBook

Reihe: International Handbooks on Information Systems

Blazewicz / Kubiak / Morzy Handbook on Data Management in Information Systems


2003
ISBN: 978-3-540-24742-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 578 Seiten, eBook

Reihe: International Handbooks on Information Systems

ISBN: 978-3-540-24742-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark



The Handbook provides practitioners, scientists and graduate students with a good overview of basic notions, methods and techniques, as well as important issues and trends across the broad spectrum of data management. In particular, the book covers fundamental topics in the field such as distributed databases, parallel databases, advanced databases, object-oriented databases, advanced transaction management, workflow management, data warehousing, data mining, mobile computing, data integration and the Web. Summing up, the Handbook is a valuable source of information for academics and practitioners who are interested in learning the key ideas in the considered area.

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Zielgruppe


Research

Weitere Infos & Material


1. Management of Data: State-of-the-Art and Emerging Trends.- 1 Introduction.- 2 Survey of the Volume.- 2. Database Systems: from File Systems to Modern Database Systems.- 1 Introduction — Database Concepts.- 2 Database System Generations.- 3 Network Database Systems.- 4 Hierarchical Database Systems.- 5 Relational Database Systems.- 6 Object-Oriented Database Systems.- 7 Federated, Mediated Database Systems and Data Warehouses.- 8 Conclusions.- 3. Data Modeling.- 1 Introduction.- 2 Early Concerns in Data Management.- 3 Abstraction in Data Modeling.- 4 Semantic Data Models.- 5 Models of Reality and Perception.- 6 Toward Cognition-Based Data Management.- 7 A Cognitive Approach to Data Modeling.- 8 Research Directions.- 4. Object-Oriented Database Systems.- 1 Introduction and Motivation.- 2 Object-Oriented Data Modeling.- 3 The Query Language OQL.- 4 Physical Object Management.- 5 Architecture of Client-Server-Systems.- 6 Indexing.- 7 Dealing with Set-Valued Attributes.- 8 Query Optimization.- 9 Conclusion.- 5. High Performance Parallel Database Management Systems.- 1 Introduction.- 2 Partitioning Strategies.- 3 Join Using Inter-Operator Parallelism.- 4 ORE: a Framework for Data Migration.- 5 Conclusions and Future Research Directions.- 6. Advanced Database Systems.- 1 Introduction.- 2 Preliminaries.- 3 Data Models and Modeling for Complex Objects.- 4 Advanced Query Languages.- 5 Advanced Database Server Capabilities.- 6 Conclusions and Outlook.- 7. Parallel and Distributed Multimedia Database Systems.- 1 Introduction.- 2 Media Fundamentals.- 3 MPEG as an Example of Media Compression.- 4 Organisation and Retrieval of Multimedia Data.- 5 Data Models for Multimedia Data.- 6 Multimedia Retrieval Sequence Using Images as an Example.- 7 Requirements for Multimedia Applications.- 8 Parallel and Distributed Processing of Multimedia Data.- 9 Parallel and Distributed Techniques for Multimedia Databases.- 10 Case Study: Cairo — Cluster Architecture for Image Retrieval and Organisation.- 11 Conclusions.- 8. Workflow Technology: the Support for Collaboration.- 1 Introduction.- 2 Application Scenario and Collaboration Requirements.- 3 Commercial Technologies Addressing Collaboration Requirements.- 4 Evaluation of Current Workflow Management Technology.- 5 Research Problems, Related Work, and Directions.- 6 Summary.- 9. Data Warehouses.- 1 Introduction.- 2 Basics.- 3 The Database of a Data Warehouse.- 4 The Data Warehouse Concept.- 5 Data Analysis of a Data Warehouse.- 6 Building a Data Warehouse.- 7 Future Research Directions.- 8 Conclusions.- 10. Mobile Computing.- 1 Introduction.- 2 Mobile Computing Infrastructure.- 3 Mobile Computing Software Architectures and Models.- 4 Disconnected Operation.- 5 Weak Connectivity.- 6 Data Delivery by Broadcast.- 7 Mobile Computing Resources and Pointers.- 8 Conclusions.- 11. Data Mining.- 1 Introduction.- 2 Mining Associations.- 3 Classification and Prediction.- 4 Clustering.- 5 Conclusions.- List of Contributors.



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