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Publications » Computers » Database Management

Advanced Database Systems

Price £55.99

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Advanced Database Systems

Carlo Zaniolo, Stefano Ceri, Christos Faloutsos, Richard Snodgrass, V.S. Subrahmanian, Roberto Zicari

ISBN 155860443X
Pages 576

Description

The database field has experienced a rapid and incessant growth since the development of relational databases. The progress in database systems and applications has produced a diverse landscape of specialized technology
areas that have often become the exclusive domain of research specialists. Examples include active databases, temporal databases, object-oriented databases, deductive databases, imprecise reasoning and queries, and multimedia information systems. This book provides a systematic introduction to and an in-depth treatment of these advanced database areas. It supplies practitioners and researchers with authoritative coverage of
recent technological advances that are shaping the future of commercial database systems and intelligent information systems.

Advanced Database Systems was written by a team of six leading specialists who have made significant contributions to the development of the technology areas covered in the book. Benefiting from the authors' long experience teaching graduate and professional courses, this book is designed to provide a gradual introduction to advanced research topics and includes many examples and exercises to support its use for individual study, desk reference, and graduate classroom teaching.

Contents
Advanced Database System by Carlo Zaniolo, Stefano Ceri, Christos Faloutsos, Richard T. Snodgrass, V.S. Subrahmanian, and Roberto Zicari Preface 1 Introduction Part I Active Databases 2 Syntax and Semantics of Active Databases 2.1 Starburst 2.1.1 Syntax of the Starburst CREATE RULE Statement 2.1.2 Semantics of Active Rules in Starburst 2.1.3 Other Active Rule Commands 2.1.4 Examples of Active Rule Executions 2.2 Oracle 2.2.1 Syntax of the Oracle CREATE TRIGGER Statement 2.2.2 Semantics of Oracles Triggers 2.2.3 Example of Trigger Executions 2.3 DB2 2.3.1 Syntax of the DB2 CREATE TRIGGER Statement 2.3.2 Semantics of DB2 Triggers 2.3.3. Examples of Trigger Executions 2.4 Chimera 2.4.1 Summary of Chimera 2.4.2 Syntax of the Chimera Define Trigger Statement 2.4.3 Semantics of Chimera Triggers 2.4.4 Examples of Trigger Executions 2.5 Taxonomy of Active Database Concepts 2.6 Bibliographic Notes 2.7 Exercises 3 Applications of Active Databases 3.1 Integrity Management 3.1.1 Rule Generation 3.1.2 Example 3.2 Derived Data Maintenance 3.2.1 Rule Generation 3.2.2 Example 3.3 Replication 3.4 Workflow Management 3.5 Business Rules 3.5.1 A Case Study: Energy Management System (EMS) 3.5.2 Database Schema for the EMS Case Study 3.5.3 Business Rules for the EMS Case Study 3.6 Bibliographic Notes 3.7 Exercises 4 Design Principles for Active Rules 4.1 Properties of Active Rule Execution 4.1.1 Termination 4.1.2 Confluence 4.1.3 Observable Determinism 4.2 Rule Modularization 4.2.1 Behavioral Stratification 4.2.2 Assertional Stratification 4.2.3 Event-Based Stratification 4.3 Rule Debugging and Monitoring 4.4 IDEA Methodology 4.4.1 Active Rule Design 4.4.2 Active Rule Prototyping 4.4.3 Active Rule Implementation 4.4.4 Design Tools Supporting the IDEA Methodology 4.5 Summary and Open Problems 4.6 Bibliographic Notes 4.7 Exercises Part II Temporal Databases 5 Overview of Temporal Databases 5.1 A Case Study 5.1.1 Temporal Projection 5.1.2 Temporal Join 5.1.3 Summary 5.2 The Time Domain 5.3 Time Data Types 5.4 Associating Facts with Time 5.4.1 Dimensionality 5.4.2 Underlying Data Model 5.4.3 Representative Data Models 5.5 Temporal Query Languages 5.6 Summary 5.7 Bibliographic Notes 5.8 Exercises 6 TSQL2 6.1 Time Ontology 6.2 Data Model 6.3 Language Constructs 6.3.1 Schema Definition 6.3.2 The SELECT Statement 6.3.3 Restructuring 6.3.4 Partitioning 6.3.5 The VALID Clause 6.3.6 The Modification Statements 6.3.7 Event Relations 6.3.8 Transaction-Time Support 6.3.9 Aggregates 6.3.10 Schema Evolution and Versioning 6.4 Other Constructs 6.5 Summary 6.6 Bibliographic Notes 6.7 Exercises 7 Implementation 7.1 System Architecture 7.2 Adding Temporal Support 7.2.1 DDL Compiler 7.2.2 Query Compiler 7.2.3 Run-Time Evaluator 7.3 Minimal support Needed for TSQL2 7.3.1 Data Dictionary and Data Files 7.3.2 DDL Compiler 7.3.3 Query Compiler 7.3.4 Run-Time Evaluator 7.3.5 Transaction and Data Manager 7.4 Summary and Open Problems 7.5 Bibliographic Notes 7.6 Exercises Part III Complex Queries and Reasoning 8 The Logic of Query Languages 8.1 Datalog 8.2 Relational Calculi 8.3 Relational Algebra 8.4 From Safe Datalog to Relational Algebra 8.4.1 Commercial Query Languages 8.5 Recursive Rules 8.6 Stratification 8.7 Expressive Power and Data Complexity 8.8 Syntax and Semantics of Datalog Languages 8.8.1 Syntax of First-Order Logic and Datalog 8.8.2 Semantics 8.8.3 Interpretations 8.9 The Models of a Program 8.10 Fixpoint-Based Semantics 8.10.1 Operational Semantics: Powers of Tp 8.11 Bibliographic Notes 8.12 Exercises 9 Implementation of Rules and Recursion 9.1 Operational Semantics: Bottom-Up Execution 9.2 Stratified Programs and Iterated Fixpoint 9.3 Differential Fixpoint Computation 9.4 Top-Down Execution 9.4.1 Unification 9.4.2 SLD-Resolution 9.5 Rule-Rewriting Methods 9.5.1 Left-Linear and Right-Linear Recursion 9.5.2 Magic Sets Method 9.5.3 The Counting Method 9.5.4 Supplementary Magic Sets 9.6 Compilation and Optimization 9.6.1 Nonrecursive Programs 9.6.2 Recursive Predicates 9.6.3 Selecting a Method for Recursion 9.6.4 Optimization Strategies and Execution Plan 9.7 Recursive Queries in SQL 9.7.1 Implementation of Recursive SQL Queries 9.8 Bibliographic Notes 9.9 Exercises 10 Database Updates and Nonmonotonic Reasoning 10.1 Nonmonotonic Reasoning 10.2 Stratification and Well-Founded Methods 10.2.1 Locally Stratified Programs 10.2.2 Well-Founded Models 10.3 Datalog (1s) and Temporal Reasoning 10.4 XY-Stratification 10.5 Updates and Active Rules 10.6 Nondeterministic Reasoning 10.7 Research Directions 10.8 Bibliographic Notes 10.9 Exercises Part IV Spatial, Text, and Multimedia Databases 11 Traditional Indexing Methods 11.1 Secondary Keys 11.1.1 Inverted Files 11.1.2 Grid File 11.1.3 k-D Trees 11.1.4 Conclusions 11.2 Spatial Access Methods (SAMs) 11.2.1 Space-Filling Curves 11.2.2 R-Trees 11.2.3 Transforming to Higher-D Points 11.2.4 Conclusions 11.3 Text Retrieval 11.3.1 Full Text Scanning 11.3.2 Inversion 11.3.3 Signature Files 11.3.4 Vector Space Model and Clustering 11.3.5 Conclusions 11.4 Summary and Future Research 11.5 Bibliographic Notes 11.6 Exercises 12 Multimedia Indexing 12.1 Basic Idea 12.2 GEMINI for Whole Match Queries 12.3 1-D Time Series 12.3.1 Distance Function 12.3.2 Feature Extraction and Lower-Bounding 12.3.3 Introduction to DFT 12.3.4 Energy-Concentrating Properties of DFT 12.3.5 Experiments 12.4 2-D Color Images 12.4.1 Image Features and Distance Functions 12.4.2 Lower-Bounding 12.4.3 Experiments and Conclusions 12.5 Subpattern Matching 12.5.1 Sketch of the Approach—ST-Index 12.5.2 Experiments 12.6 Summary and Future Research 12.7 Bibliographic Notes 12.8 Exercises Part V Uncertainty in Databases and Knowledge Bases 13 Models of Uncertainty 13.1 Introduction 13.1.1 Uncertainty in DBs: An Image Database Example 13.1.2 Uncertainty in DBs: A Temporal Database Example 13.1.3 Uncertainty in DBs: A Null-Value Example 13.2 Models of Uncertainty 13.2.1 Fuzzy Sets 13.2.2 Lattice-Based Approaches 13.2.3 Relationship to Fuzzy Logic 13.2.4 Probability Theory 13.3 Bibliographic Notes 13.4 Exercises 14 Uncertainty in Relational Databases 14.1 Lattice-Based Relational Databases 14.1.1 An Example 14.1.2 Querying Lattice-Based Databases 14.2 Probabilistic Relational Databases 14.2.1 Probabilistic Relations 14.2.2 Annotated Relations 14.2.3 Converting Probabilistic Tuples to Annotated Tuples 14.2.4 Manipulating Annotated Relations 14.2.5 Querying Probabilisitc Databases 14.3 Bibliographic Notes 14.4 A Final Note 14.5 Exercises 15 Including Uncertainty in Deductive Databases 15.1 Generalized Annotated Programs (GAPs) 15.1.1 Lattice-Based KBs: Model Theory 15.1.2 Lattice-Based KBs: Fixpoint Theory 15.1.3 Lattice-Based KBs: Query Processing 15.2 Probabilisic Knowledge Bases 15.2.1 Probabilistic KBs: Fixpoint Theory 15.2.2 Probabilistic KBs: Model Theory 15.2.3 Probabilistic KBs: Query Processing 15.3 Bibliographic Notes 15.4 Research Directions 15.5 Summary 15.6 Exercises Part VI Schema and Database Evolution in Object Database Systems 16 Object Databases and Change Management 16.1 Why Changes Are Needed 16.2 The Essence of Object Databases 16.2.1 Basics of Object Databases 16.2.2 Standards 16.2.3 Change Management in Object Database Systems 16.3 Bibliographic Notes 16.4 Exercises 17 How to Change the Schema 17.1 Changing the Schema Using Primitives 17.1.1 Taxonomy of Schema Modifications 17.1.2 Schema Evolution Primitives in O2 17.2 Schema Invariants 17.3 Semantics of Schema Modifications 17.4 Bibliographic Notes 17.5 Exercises 18 How to Change the Database 18.1 Immediate vs. Deferred Transformations 18.1.1 Immediate Database Trasformation 18.1.2 Deferred Database Transformation 18.2 Preserving Structural Consistency 18.2.1 Structural Consistency Preserving Primitives 18.2.2 Structural Consistency Modifying Primitives 18.3 User-Defined and Default Transformations 18.3.1 Default Database Transformations 18.3.2 User-Defined Database Transformations 18.3.3 User-Defined Object Migration Functions 18.4 Implementing Database Updates in O2 18.4.1 Problems with Deferred Database Transformations 18.4.2 Data Structures 18.4.3 the Deferred Database Update Algorithm 18.5 Related Work 18.6 Bibliographic Notes 18.7 Exercises 19 How to Change the Database Fast 19.1 Factors Influencing the Performance of a Database Transformation 19.1.1 Immediate Database Tranformation 19.1.2 Deferred Transformation 19.1.3 Hybrid 19.2 How to Benchmark Database Updates 19.2.1 Basic Benchmark Organization 19.2.2 How to Run the Benchmark 19.3 Performance Evaluation 19.3.1 Small Databases 19.3.2 Large Databases 19.4 Open Problems 19.5 Bibliographic Notes Bibliography Author Index Subject Index Series: The Morgan Kaufmann Series in Data Management Systems