Information Systems

The Objective of Database Management.- 1. A Shared Database.- 2. Database Integrity.- 2.1. Facets of Database Integrity.- 2.2. The Means to Database Integrity.- 3. Availability.- 3.1. Diversity of Users.- 3.2. Diversity of Modes.- 3.3. Diversity of Languages.- 3.4. Diversity of Needs.- 4. Evolvability.- 4.1. Changing Technology.- 4.2. Changing User Demands.- 4.3. The Means to Evolvability.- 5. References.- 6. Bibliography.- 6.1. Articles.- 6.2. Books and Major Works.- Relational Data Base Systems: A Tutorial.- 1. Introduction.- 2. The Relational Model of Data.- 3. A Sample Data Model.- 4. The Hierarchical Approach.- 5. The Network Approach.- 6. A Data Sublanguage for the Relational Model.- 6.1. Relational Algebra.- 6.2. Relational Calculus.- 7. Some Existing Relational Systems.- 8. References.- A Relational Data Management System.- 1. Introduction.- 2. Example.- 3. Application.- 4. Implementation.- 5. Reflections.- 6. References.- A Data Base Search Problem.- 1. Introduction.- 1.1. Background.- 1.2. Queries.- 1.3. Assumptions.- 1.4. General Plan.- 1.5. Summary.- 2. Representation of a Query.- 2.1. Introduction.- 2.2. Normalization of ß-Expressions.- 2.3. Graphic Representation of a Query.- 2.4. Tabular Representation of a Query.- 2.5. Conclusion.- 3. Improvement of the Reduction Algorithm.- 3.1. Introduction.- 3.2. The Codd Reduction Algorithm.- 3.3. The Evaluation Factors.- 3.4. Improvements on Reduction Algorithm.- 3.5. The Join Algorithm.- 3.6. Improved Reduction Algorithm.- 3.7. Summary.- 4. Algorithm Using Semi-Joins.- 4.1. Introduction.- 4.2. The Semi-Join.- 4.3. The Indirect Join.- 4.4. Target Relations Determined by the T-Table.- 4.5. Exploring a Relation.- 4.6. Estimating Intermediate Storage.- 4.7. The Algorithm Using Semi-Joins.- 4.8. Summary.- 5. Conclusion.- 6. Appendix A. Relational Calculus.- 7. Appendix B. Justification for Reduced Ranges.- 8. References.- An Experiment with a Relational Data Base System in Environmental Research.- 1. Introduction.- 1.1. An Environmental Research Problem.- 1.2. Project Background.- 1.3. Problem Characteristics.- 2. Data Processing in an Ecological Research Program.- 2.1. What Activities Are Involved?.- 2.2. Demands on the Software System.- 3. Computer Techniques in the Project.- 3.1. Information Systems Used.- 3.2. Characteristics of IS/1.- 3.3. Some Experiences.- 3.4. An Example.- 4. Conclusion.- 5. References.- Special Topic Data Base Development.- 1. Introduction.- 1.1. Content-Induced Partition.- 1.2. Profile-Directed Partition.- 1.3. Data Base Organization.- 2. Content-Induced Partition.- 2.1. Characteristic Weighting Algorithm.- 2.2. Logicostatistical Term Associations.- 2.3. Retrieval Implications.- 3. Profile-Directed Partition.- 3.1. Topic Profile Generation.- 3.2. Term Association Submatrix Partition.- 3.3. Retrieval Implications.- 4. Data Base Organization. Retrieval File Structures.- 5. Summary.- 6. References.- BOLTS: A Retrieval Language for Tree-Structured Data Base Systems.- 1. Introductory Remarks.- 2. Preliminary Definitions.- 3. Retrieval Procedure.- 4. Examples of Retrievals in SET-BARS and TREE-BARS.- 4.1. An Example of the Set-Theoretic System.- 4.2. An Example of the Tree-Theoretic System.- 5. Definition of BOLTS.- 5.1. Set Manipulation Functions.- 5.2. Node Extraction Functions.- 5.3. Selection and Qualification in BOLTS.- 5.4. Examples of SELECT, ADJUST, QUALIFY, and TYPE.- 6. Tree Operations in BOLTS.- 6.1. Preliminary Theorems.- 6.2. Tree Intersection in BOLTS.- 6.3. Tree Complement in BOLTS.- 6.4. Examples of Tree Operations in BOLTS.- 7. The “HAS Clause” in BOLTS.- 7.1. An Example of Sibling Retrieval.- 7.2. An Example of Indirect Ancestor Retrieval.- 7.3. An Additional Capability in BOLTS.- 8. Concluding Remarks.- 9. References.- An Algorithm for Maintaining Dynamic AVL Trees.- 1. Introduction.- 2. AVL Trees.- 3. Searching.- 4. Insertion.- 5. Deletion.- 6. The Implemented Algorithm.- 7. Comparison with Binary Search Trees of Bounded Balance. ..- 8. References.- SPIRAL’s Autoindexing and Searching Algorithms.- 1. Introduction.- 2. Indexing and Storage System.- 2.1. Exclusion Words.- 2.2. Suffix Truncation.- 2.3. Encoding for Vocabulary Indices.- 2.4. Encoding for Word Usage Patterns.- 3. Inquiry Form.- 4. Inquiry Compilation.- 5. Retrieval Process.- 5.1. Type 1 Processing.- 5.2. Type 3 Processing.- 5.3. Type 5 Processing.- 5.4. Type 7 Processing.- 6. Conclusion.- 7. References.- SEFIRE : A Sequential Feedback Interactive Retrieval System.- 1. Introduction.- 2. Characteristics of Interactive Information Retrieval System. ..- 3. Hierarchical Category Files.- 4. Software Design.- 4.1. Design Principles.- 4.2. System Tables.- 5. Experimental Results.- 6. Conclusions.- 7. References.- An Analysis of Document Retrieval Systems Using a Generalized Model.- 1. Introduction.- 2. The Generalized Model.- 2.1. User.- 2.2. Logical Processor.- 2.3 Selector.- 2.4. Descriptor File.- 2.5. Locator.- 2.6. Document File.- 2.7. Data.- 2.8. Analysis.- 3. Analysis of Implemented Systems.- 3.1. Query System.- 3.2. GIPSY.- 3.3. BIRS.- 3.4. SMART.- 4. Summary.- 5. References.- Information Systems for Urban Problem Solvers.- 1. Introduction : Recognition of a Need for Urban Information Systems.- 2. A Typology of Problems : Information Systems for Problem- Solving.- 3. Information Systems for Well-Defined Problems.- 4. Functions of an Information System for Ill-Structured Problems.- 5. Design Principles.- 6. Conclusions and Recommendations.- 7. Appendix A: A Model for the Simplest Shopping Problem....- 8. Appendix B: Consequences of a Decision by People Who Have Undesirable Genes Not to Have Offspring.- 9. References.- EMISARI: A Management Information System Designed to Aid and Involve People.- 1 Introduction.- 2. Description of System.- 2.1. User’s Guide, Description, and Explanation Choices.- 2.2. Agencies and Contacts.- 2.3. Messages and Communication.- 2.4. Estimates, Programs, and Tables.- 2.5. Text Files.- 2.6. Special Features.- 3. Role of the Monitor.- 4. Implementation Features.- 4.1. Use of XBASIC.- 4.2. Files and Adaptive Index.- 4.3. Data Survivability.- 5. References.- Transferability and Translation of Programs and Data.- 1. Introduction.- 2. Aspects of Language Translation.- 3. Aspects of Data Translation.- 3.1. Definitions of Data Terms.- 3.2. A Model of Data Accessing.- 3.3. Generalized Data Access and Translation.- 4. Interpendence of Program and Data Translation.- 5. Features of Program and Data Translation.- 5.1. Logical Elements of a Program Translator.- 5.2. Logical Elements of a Data Translator.- 5.3. Uniqueness of Translation.- 6. Conclusions.- 7. References.- Processing Systems Optimization through Automatic Design and Reorganization of Program Modules.- 1. Introduction.- 2. Methodology.- 3. Definitions.- 4. Process Grouping Concept.- 5. Process Grouping Determination.- 5.1. Generation of Feasible Process Groupings to Form Modules.- 5.2. Generation of Alternative System Designs.- 5.3. Transport Volume Savings Calculation.- 6. Combining Processes.- 7. Example.- 8. Conclusions.- 9. References.- Verification and Checking of APL Programs.- 1. Introduction.- 2. Proving Assertions about APL Programs.- 3. Verification of Constraints of APL Programs.- 3.1. Straight-Line Programs with Assertions.- 3.2. Programs with Branches and Assertions.- 3.3. Programs with Branches and No Assertions.- 4. Summary and Conclusions.- 5. References.- G/PL/I: Extending PL/I for Graph Processing.- 1. Introduction.- 2. An Informal Description of the Extension.- 3. Implementation Considerations.- 4. An Example.- 5. Directions for Further Developments.- 6. Appendix.- 7. References.- A Unified Approach to the Evaluation of a Class of Replacement Algorithms.- 1. Introduction.- 2. Definition of Basic Concepts.- 3. Random, Partially Preloaded Algorithms.- 4. Proof of Theorem 2.- 5. The Algorithms RAND and FIFO.- 6. Appendix. Proof of Lemma 1.- 7. References.- Quantitative Timing Analysis and Verification for File Organization Modeling.- 1. Introduction.- 2. General Description and Organization of the Model.- 3. Techniques of Analysis.- 4. Experimental Evaluation of the Timing Equations.- 5. Conclusion.- 6. References.- A Mathematical Model for Computer-Assisted Document Creation.- 1. Introduction.- 2. Description of the Model and Its Mathematical Representation.- 3. Optimal Operation.- 4. A Special Case: “Ideal Operator—Exponential File”.- 5. Application to System Design.- 6. Conclusions.- 7. Appendix.- 8. References.- Representing Geographic Information for Efficient Computer Search.- 1. Introduction.- 1.1. Subject.- 1.2. Examples.- 2. Representation Technique.- 2.1. Basic Data Structure.- 2.2. Properties of the TCB Structure.- 2.3. Representing Regional Information.- 2.4. Representing Contour Map Information.- 3. Retrieval Applications.- 3.1. Geographic Information System.- 3.2. Terrain Coverage Information for Microwave Radiometer Image Prediction Model.- 3.3. Terrain Relief Information for Radar Image Prediction Model.- 4. Summary.- 5. Appendix Contour Map Search List Determination.- 6. References.- A Syntactic Pattern Recognition System with Learning Capability.- I. Introduction.- 2. Design Concepts and Overall System Description.- 3. Learning of Pattern Grammar.- 4. Learning of Production Probabilities.- 5. Computational Results.- 6. Conclusion.- 7. References.- Optimization in Nonhierarchic Clustering.- 1. Introduction.- 1.1. The Problem.- 1.2. The Dynamic Clusters Method.- 1.3. Synthetic Study of the Solutions Obtained.- 2. Some Notations and Definitions.- 3. Constructing the Triplets (f,g, W).- 3.1. General Formulation.- 3.2. The Different Variants and a Comparison of Some of Interest.- 3.3. Construction of Triplets That Make the Sequence un Decreasing.- 4. The Structure of, Lk, Pk, Vk and Optimality Properties.- 4.1. The Nonbiased Elements.- 4.2. The Impasse Elements.- 5. Searching for Invariants.- 5.1. Measure of the Rooted Trees.- 5.2. Strong Forms, Fuzzy Sets, and Information.- 5.3. Global Optimum of Vk.- 5.4. Approaching the Global Optimum by Changing Trees.- 6. Programming the Tables of the Strong Forms and the Heuristic Interpretation.- 7. Examples of Applications.- 7.1. The Artificial Example of Ruspini.- 7.2. Classifying the Soundings of a Mine for Its Minerals.- 7.3. Study of Serum Protein Disturbance in Clinical Pathology.- 8. Conclusion.- 9. Appendix A.- 10. Appendix B.- 11. Appendix C.- 12. References.- Nonparametric Learning Using Contextual Information.- 1. Introduction.- 2. Structure of the Machine.- 3. Nonparametric Learning.- 4. Computer Simulation.- 5. Concluding Remarks.- 6. References.