Information Systems

Ten years ago the first International Symposium on Computer and Information Sciences (COINS-63) was held at Northwestern University. Since that time, computer and information sciences have witnessed a great intensification of research and education. The activities in this field have been significantly broadened and enriched. During this ten-year period, we have organized four COINS symposia to provide a forum for promoting com­ munication among scientists, engineers, and educators in the computer and information science field and to act as a catalyzer for stimulating creative thinking within the community of information processing. The COINS-72 symposium, which took place in Miami Beach on December 14--16,1972, under the cosponsorship of the U.S. Army Research Office, the Atomic Energy Commission, and the University of Florida, is the fourth International Symposium on Computer and Information Sciences. The theme of this COINS symposium is information systems. This theme has been selected for the following reasons: Information systems have offered widespread applications in education, government, industry, and science. The bulk of research in computer and information science is now geared to the development of improved information systems. A major portion of software engineering is concerned with computer software and sophisticated information system design. It seems logical that a symposium on information systems should follow the preceding software engineering conference.

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.