Volume 9 of this series on information systems science presents four timely topics of current interest in this growing field. In each chapter an attempt is made to familiarize the reader with some basic background information on the advances discussed, so that this volume may be used independently or in conjunction with the previous volumes. The emphasis in this volume is on data structures for scene analysis, database management technology, inductive inference in processing pattern-based information, and logic design of MOS networks. Scene analysis has become a very important aspect in…mehr
Volume 9 of this series on information systems science presents four timely topics of current interest in this growing field. In each chapter an attempt is made to familiarize the reader with some basic background information on the advances discussed, so that this volume may be used independently or in conjunction with the previous volumes. The emphasis in this volume is on data structures for scene analysis, database management technology, inductive inference in processing pattern-based information, and logic design of MOS networks. Scene analysis has become a very important aspect in information system design. The process of scene analysis involves sensing, segmentation, recognition, and interpretation. Innovative development of algorithms for these tasks requires the utilization of structural relationship prevalent within the sensed data. In Chapter 1, Thomason and Gonzalez discuss the formula tion of data representation techniques and the properties of data structures and databases in scene analysis. In view of the growing importance of database management, Chapter 2 is devoted to an overview of database management technology. In this chapter Kobayashi covers a variety of current topics. The topics discussed include system design methodology, data structure theory, semantic con siderations, calculus-based database operations, database management functions, and the issues of integrity, security, concurrency, and recoverabil ity. This chapter also discusses the end-user languages and several existing database management systems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1 Data Structures and Databases in Digital Scene Analysis.- 1. Introduction.- 2. Data Structures.- 2.1. Definitions and Basic Concepts.- 2.2. Use of Lists for Scene Representation and Processing.- 2.3. Use of Quad Trees for Image representation and Processing.- 3. Databases.- 3.1. Definitions and Basic Concepts.- 3.2. Hierarchical and Relational Models for Scene Representation and Processing.- 3.3. Use of Relational Tables for Three-Dimensional Object Location.- 4. Examples of Existing Systems.- 4.1. Multisensor Image Database System (MIDAS).- 4.2. Relational Pictorial Database System.- 5. Summary.- References.- 2 An Overview of Database Management Technology.- 1. Introduction.- 2. Motivations.- 2.1. Large Shared Files.- 2.2. Rapid Social Change.- 2.3. Man-Computer Cooperation.- 3. Database as a New Systems Methodology.- 3.1. Output-Oriented Approach.- 3.2. Database-Oriented Approach.- 4. Logical Database Structure.- 4.1. Relational View of the Real World.- 4.2. Geometric Representation of Relations.- 4.3. Semantic Constraints.- 4.4. Tuple Constraints.- 4.5. Dependencies.- 4.6. Interrelation Constraints.- 4.7. Other Static Constraints.- 4.8. Definition of Logical Database Structure.- 5. Database Operations.- 5.1. Functions of Tuples.- 5.2. Alpha Operation.- 5.3. Relational Algebra.- 5.5. Information Algebra.- 5.6. Imaginary Tuples.- 5.7. Navigations.- 5.8. Disadvantages of Set Operations.- 5.9. Tuple-by-Tuple Operations.- 5.10. Data Manipulation Requirements.- 6. Other Requirements.- 6.1. Integrity.- 6.2. Security.- 6.3. Concurrency.- 6.4. Recoverability.- 6.5. Database Distribution.- 6.6. Environmental Requirements.- 7. Physical Representation of the Database.- 7.1. Data Associations in Computer Storage.- 7.2. Basic File Organizations.- 7.3. Representation of Entity Relations.- 7.4. Representation of Relationship Relations.- 7.5. Localization.- 8. Database Management Functions.- 8.1. Data Description.- 8.2. Data Manipulation.- 8.3. Binding.- 8.4. Functions for Database Administrators.- 8.5. Database Management Languages.- 9. Database Management Systems.- 9.1. Selection Criteria for Database Management Systems.- 9.2. Directory-Type Classification of Database Management Systems.- 9.3. Database Operations.- 9.4. Environmental Requirements.- 9.5. Physical Representation.- 9.6. Host Language Interfaces.- 9.7. Other Criteria.- 10. End-User Languages.- 10.1. Application Routines.- 10.2. Language for Real-Time Users.- 10.3. Languages for Casual Users.- 10.4. Languages for Parameter Users.- 10.5. Self-Contained Systems.- 10.6. High-Level Languages on the Host Language Systems.- 10.7. Language Extensibility.- 11. Future Research Directions.- 11.1. Database Machines.- 11.2. Deductive Processes.- 11.3. Neutral-Language Query Processing.- References.- 3 Processing of Pattern-Based Information, Part I: Inductive Inference Methods Suitable for use in Pattern Recognition and Artificial Intelligence.- 1. Introduction.- 1.1. Preamble.- 1.2. The Decision Rule Inference Problem in Pattern Recognition.- 1.3. Inference in Artificial Intelligence.- 1.4. Basic Tenets of the Present Approach to Inductive Inference of Decision Rules.- 2. Representation of Patterns.- 3. Algorithm For Decision Rule Inference.- 3.1. The Nature of the Task.- 3.2. Some Concepts and Definitions Used in the Algorithms.- 3.3. Hypotheses Generation and Test.- 3.4. Problem Reduction in the CD Hypothesis Generation.- 3.5. Sentential Form of the Decision Rules.- 3.6. An Illustration of Suggested Algorithm.- 4. Structure of the Controls for Systematic Implementation of the Inference Procedure.- 5. A Brief description of the Implementation.- 6. Examples.- References.- 4 Processing of Pattern-Based Information, Part II: Description of Inductive Inference in Terms of Transition Networks.- 1. Introduction.- 2. Description of the Inductive Inference Transition Network.- 3. An Inference Algorithm Represented by an Inductive Inference Transition Network.- 3.1. A Brief Description of the
1 Data Structures and Databases in Digital Scene Analysis.- 1. Introduction.- 2. Data Structures.- 2.1. Definitions and Basic Concepts.- 2.2. Use of Lists for Scene Representation and Processing.- 2.3. Use of Quad Trees for Image representation and Processing.- 3. Databases.- 3.1. Definitions and Basic Concepts.- 3.2. Hierarchical and Relational Models for Scene Representation and Processing.- 3.3. Use of Relational Tables for Three-Dimensional Object Location.- 4. Examples of Existing Systems.- 4.1. Multisensor Image Database System (MIDAS).- 4.2. Relational Pictorial Database System.- 5. Summary.- References.- 2 An Overview of Database Management Technology.- 1. Introduction.- 2. Motivations.- 2.1. Large Shared Files.- 2.2. Rapid Social Change.- 2.3. Man-Computer Cooperation.- 3. Database as a New Systems Methodology.- 3.1. Output-Oriented Approach.- 3.2. Database-Oriented Approach.- 4. Logical Database Structure.- 4.1. Relational View of the Real World.- 4.2. Geometric Representation of Relations.- 4.3. Semantic Constraints.- 4.4. Tuple Constraints.- 4.5. Dependencies.- 4.6. Interrelation Constraints.- 4.7. Other Static Constraints.- 4.8. Definition of Logical Database Structure.- 5. Database Operations.- 5.1. Functions of Tuples.- 5.2. Alpha Operation.- 5.3. Relational Algebra.- 5.5. Information Algebra.- 5.6. Imaginary Tuples.- 5.7. Navigations.- 5.8. Disadvantages of Set Operations.- 5.9. Tuple-by-Tuple Operations.- 5.10. Data Manipulation Requirements.- 6. Other Requirements.- 6.1. Integrity.- 6.2. Security.- 6.3. Concurrency.- 6.4. Recoverability.- 6.5. Database Distribution.- 6.6. Environmental Requirements.- 7. Physical Representation of the Database.- 7.1. Data Associations in Computer Storage.- 7.2. Basic File Organizations.- 7.3. Representation of Entity Relations.- 7.4. Representation of Relationship Relations.- 7.5. Localization.- 8. Database Management Functions.- 8.1. Data Description.- 8.2. Data Manipulation.- 8.3. Binding.- 8.4. Functions for Database Administrators.- 8.5. Database Management Languages.- 9. Database Management Systems.- 9.1. Selection Criteria for Database Management Systems.- 9.2. Directory-Type Classification of Database Management Systems.- 9.3. Database Operations.- 9.4. Environmental Requirements.- 9.5. Physical Representation.- 9.6. Host Language Interfaces.- 9.7. Other Criteria.- 10. End-User Languages.- 10.1. Application Routines.- 10.2. Language for Real-Time Users.- 10.3. Languages for Casual Users.- 10.4. Languages for Parameter Users.- 10.5. Self-Contained Systems.- 10.6. High-Level Languages on the Host Language Systems.- 10.7. Language Extensibility.- 11. Future Research Directions.- 11.1. Database Machines.- 11.2. Deductive Processes.- 11.3. Neutral-Language Query Processing.- References.- 3 Processing of Pattern-Based Information, Part I: Inductive Inference Methods Suitable for use in Pattern Recognition and Artificial Intelligence.- 1. Introduction.- 1.1. Preamble.- 1.2. The Decision Rule Inference Problem in Pattern Recognition.- 1.3. Inference in Artificial Intelligence.- 1.4. Basic Tenets of the Present Approach to Inductive Inference of Decision Rules.- 2. Representation of Patterns.- 3. Algorithm For Decision Rule Inference.- 3.1. The Nature of the Task.- 3.2. Some Concepts and Definitions Used in the Algorithms.- 3.3. Hypotheses Generation and Test.- 3.4. Problem Reduction in the CD Hypothesis Generation.- 3.5. Sentential Form of the Decision Rules.- 3.6. An Illustration of Suggested Algorithm.- 4. Structure of the Controls for Systematic Implementation of the Inference Procedure.- 5. A Brief description of the Implementation.- 6. Examples.- References.- 4 Processing of Pattern-Based Information, Part II: Description of Inductive Inference in Terms of Transition Networks.- 1. Introduction.- 2. Description of the Inductive Inference Transition Network.- 3. An Inference Algorithm Represented by an Inductive Inference Transition Network.- 3.1. A Brief Description of the
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