Approved for public release, distribution unlimited / This thesis is primarily concerned with automation support for an organization in charge of the construction and modification of buildings for military bases and civilian construction during disaster relief. The first issue at hand is the need to know how this organization functions manually and the participation of each department in daily work. Use Case Analysis was applied to understand the business process and an UML model was created to appraise the domain concepts. Architecture for a decision support system was then developed to provide the necessary automation support and a prototype for the user interface of the proposed system was constructed to evaluate the architectural design. The proposal software will improve the decision-making ability of the leader of this organization and the heads of each department. It will make the routine tasks easier, and provide the necessary and accurate data in a timely manner. / Captain, Tunisia Army

Approved for public release; distribution is unlimited. / The U.S. Army's Future Force is being developed as a faster, lighter, more rapidly deployable alternative to the current force structure. The Future Force will feature a smaller in-theater footprint and require the ability to cover a larger area of the battle space with intelligence-gathering assets. To support this development the Naval Postgraduate School and TRAC Monterey began to conduct research in the area of allocation of Future Force sensor platforms. A previous thesis developed the Sensor Allocation Model (SAM) for finding an appropriate mix and allocation strategy for organic Unit of Action sensors in a given threat scenario. The mix suggested by the model is robust to uncertainties in sensor performance and target quantity and location. SAM shows great promise for use as a screening tool in support of analysis of alternatives studies as well as in support of Army and Joint war fighting experimentation. It also has potential for use as an operational decision support tool for unit commanders. This thesis discusses three improvements to SAM. First, SAM has been translated into a programming language that easily can be implemented into any simulation environment. Second, it now contains more realistic constraints on sensor platform employment duration and distance. Third, the model estimates of sensor performance have been improved with a Probability Line of Sight model. Together, these improvements have greatly improved SAM's usability. / Captain, German Army

Typescript. / Digitized by Kansas Correctional Industries

Call number: LD2668 .T4 1986 P42 / Master of Science / Computing and Information Sciences

Database management systems (DBMSs) are designed with the goal set to compile SQL queries to physical plans that, when executed, provide results to the SQL queries. Building on this functionality, an ever-increasing number of application domains (e.g., provenance management, online query optimization, physical database design, interactive data profiling, monitoring, and interactive data visualization) seek to operate on how queries are executed by the DBMS for a wide variety of purposes ranging from debugging and data explanation to optimization and monitoring. Unfortunately, DBMSs provide little, if any, support to facilitate the development of this class of important application domains. The effect is such that database application developers and database system architects either rewrite the database internals in ad-hoc ways; work around the SQL interface, if possible, with inevitable performance penalties; or even build new databases from scratch only to express and optimize their domain-specific application logic over how queries are executed. To address this problem in a principled manner in this dissertation, we introduce a prototype DBMS, namely, Smoke, that exposes instrumentation mechanisms in the form of a framework to allow external applications to manipulate physical plans. Intuitively, a physical plan is the underlying representation that DBMSs use to encode how a SQL query will be executed, and providing instrumentation mechanisms at this representation level allows applications to express and optimize their logic on how queries are executed. Having such an instrumentation-enabled DBMS in-place, we then consider how to express and optimize applications that rely their logic on how queries are executed. To best demonstrate the expressive and optimization power of instrumentation-enabled DBMSs, we express and optimize applications across several important domains including provenance management, interactive data visualization, interactive data profiling, physical database design, online query optimization, and query discovery. Expressivity-wise, we show that Smoke can express known techniques, introduce novel semantics on known techniques, and introduce new techniques across domains. Performance-wise, we show case-by-case that Smoke is on par with or up-to several orders of magnitudes faster than state-of-the-art imperative and declarative implementations of important applications across domains. As such, we believe our contributions provide evidence and form the basis towards a class of instrumentation-enabled DBMSs with the goal set to express and optimize applications across important domains with core logic over how queries are executed by DBMSs.

by Ma Wai Yan. / Includes bibliographical references / Thesis (M.Ph.)--Chinese University of Hong Kong, 1985

by Chang Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 111-115). / Acknowledgement --- p.i / Abstract --- p.ii / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Outline of the Work of This Thesis --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.5 / Chapter 2 --- REVIEW OF RELATED WORKS --- p.6 / Chapter 2.1 --- Deduce2 --- p.6 / Chapter 2.2 --- ARES --- p.8 / Chapter 2.3 --- VAGUE --- p.10 / Chapter 2.4 --- Fuzzy Sets-Based Approaches --- p.12 / Chapter 2.5 --- Some General Remarks --- p.14 / Chapter 3 --- A FUZZY DATABASE QUERY LANGUAGE --- p.18 / Chapter 3.1 --- Basic Concepts of Fuzzy Sets --- p.18 / Chapter 3.2 --- The Syntax of the Fuzzy Query Language --- p.21 / Chapter 3.3 --- Fuzzy Operators --- p.25 / Chapter 3.3.1 --- AND --- p.27 / Chapter 3.3.2 --- OR --- p.27 / Chapter 3.3.3 --- COMB --- p.28 / Chapter 3.3.4 --- POLL --- p.28 / Chapter 3.3.5 --- HURWICZ --- p.30 / Chapter 3.3.6 --- REGRET --- p.31 / Chapter 4 --- SYSTEM DESIGN --- p.35 / Chapter 4.1 --- General Requirements and Definitions --- p.35 / Chapter 4.1.1 --- Requirements of the system --- p.36 / Chapter 4.1.2 --- Representation of membership functions --- p.38 / Chapter 4.2 --- Overall Architecture --- p.41 / Chapter 4.3 --- Interface --- p.44 / Chapter 4.4 --- Knowledge Base --- p.46 / Chapter 4.5 --- Parser --- p.51 / Chapter 4.6 --- ORACLE --- p.52 / Chapter 4.7 --- Data Manager --- p.53 / Chapter 4.8 --- Fuzzy Processor --- p.57 / Chapter 5 --- IMPLEMENTION --- p.59 / Chapter 5.1 --- Some General Considerations --- p.59 / Chapter 5.2 --- Knowledge Base --- p.60 / Chapter 5.2.1 --- Converting a concept into conditions --- p.60 / Chapter 5.2.2 --- Concept trees --- p.62 / Chapter 5.3 --- Data Manager --- p.64 / Chapter 5.3.1 --- Some issues on the implementation --- p.64 / Chapter 5.3.2 --- Dynamic library --- p.67 / Chapter 5.3.3 --- Precompiling process --- p.68 / Chapter 5.3.4 --- Calling standard --- p.71 / Chapter 6 --- CASE STUDIES --- p.76 / Chapter 6.1 --- A Database for Job Application/Recruitment --- p.77 / Chapter 6.2 --- Introduction to the Knowledge Base --- p.79 / Chapter 6.3 --- Cases --- p.79 / Chapter 6.3.1 --- Crispy queries --- p.79 / Chapter 6.3.2 --- Fuzzy queries --- p.82 / Chapter 6.3.3 --- Concept queries --- p.85 / Chapter 6.3.4 --- Fuzzy Match --- p.87 / Chapter 6.3.5 --- Fuzzy operator --- p.88 / Chapter 7 --- CONCLUSION --- p.93 / Appendix A Sample Data in DATABASE --- p.96 / Bibliography --- p.111

Wai-Tung Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1994. / Includes bibliographical references (leaves 102-104). / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Central Processing Unit innovation / Chapter 1.2 --- Long Instruction Word computer / Chapter 1.3 --- Prior attempts / Chapter 2. --- The new architecture --- p.11 / Chapter 2.1 --- The triple-instruction word / Chapter 2.2 --- Functional view of the architecture / Chapter 2.3 --- Inter-functional units synchronization / Chapter 2.4 --- Instruction set design / Chapter 2.5 --- Special features / Chapter 3. --- Simulation of the architecture --- p.39 / Chapter 3.1 --- Computer architecture simulation / Chapter 3.2 --- The simulation language used: APL / Chapter 3.3 --- Simulation environment / Chapter 3.4 --- Simulation design / Chapter 3.5 --- The micro-architecture / Chapter 3.6 --- Implementation details / Chapter 4. --- The supporting environment --- p.53 / Chapter 4.1 --- The environment / Chapter 4.2 --- The Pseudo-machine configuration / Chapter 4.3 --- Assembly language description / Chapter 4.4 --- Details of the utilities / Chapter 5. --- Evaluation --- p.53 / Chapter 5.1 --- Case Study / Chapter 5.2 --- Results and comparison / Chapter 5.3 --- Summary / Chapter 6. --- Discussion and conclusion --- p.96 / Chapter 6.1 --- The triple-instruction computer / Chapter 6.2 --- Use of APL for architectural simulation / Chapter 6.3 --- Further considerations / Chapter 7. --- References --- p.81 / Chapter 8. --- Appendix I: Program listing for the TIC simulator / Chapter 9. --- Appendix II: Screen dump of the simulation runs

by Au Sai Kit. / Thesis (M.A.Ed.)--Chinese University of Hong Kong, 1992. / Includes bibliographical references (leaves 79-83). / ACKNOWLEDGEMENT --- p.ii / ABSTRACT --- p.iii / LIST OF TABLES --- p.vii / LIST OF FIGURES --- p.ix / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Purpose of the research --- p.2 / Chapter 1.2 --- Significance of the research --- p.4 / Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.5 / Chapter 2.1 --- Literature related to cognitive skills in programming --- p.5 / Chapter 2.2 --- Literature related to programming in BASIC --- p.9 / Chapter 2.3 --- Literature related to organization aids --- p.13 / Chapter 2.4 --- Literature related to methodology --- p.23 / Chapter CHAPTER 3 --- METHODOLOGY --- p.28 / Chapter 3.1 --- Theoretical framework --- p.28 / Chapter 3.2 --- Hypotheses --- p.33 / Chapter 3.3 --- Method --- p.34 / Chapter 3.3.1 --- Procedure --- p.34 / Chapter 3.3.2 --- Subjects --- p.35 / Chapter 3.3.3 --- Instruments --- p.35 / Chapter 3.3.4 --- Design --- p.39 / Chapter 3.3.5 --- Analysis --- p.44 / Chapter CHAPTER 4 --- RESULTS AND DISCUSSION --- p.46 / Chapter 4.1 --- Reliability of the instruments --- p.46 / Chapter 4.2 --- Results and discussion --- p.49 / Chapter CHAPTER 5 --- CONCLUSIONS AND RECOMMENDATIONS --- p.71 / Chapter 5.1 --- Summary of findings --- p.71 / Chapter 5.2 --- Conclusions --- p.73 / Chapter 5.3 --- Limitations --- p.75 / Chapter 5.4 --- Recommendations --- p.77 / Bibliography --- p.79

by Chat Siu Wing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 104-107. / ABSTRACT --- p.i / ACKNOWLEDGEMENTS --- p.ii / TABLE OF CONTENTS --- p.iii / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter CHAPTER 2 --- TEMPORAL DATABASES --- p.5 / Chapter 2.1 --- The Importance of Temporal Data --- p.5 / Chapter 2.2 --- Incorporating Time in Databases --- p.6 / Chapter 2.2.1 --- Time Dimensions --- p.6 / Chapter 2.2.2 --- Classification --- p.6 / Chapter 2.2.2.1 --- Snapshot Databases --- p.6 / Chapter 2.2.2.2 --- Rollback Databases --- p.7 / Chapter 2.2.2.3 --- Historical Databases --- p.8 / Chapter 2.2.2.4 --- Temporal Databases --- p.9 / Chapter 2.2.3 --- Current Research Areas --- p.9 / Chapter 2.2.3.1 --- Time Semantics at the Conceptual Level --- p.10 / Chapter 2.2.3.2 --- Temporal Data Model --- p.11 / Chapter 2.2.3.3 --- Temporal Query Language --- p.11 / Chapter CHAPTER 3 --- CONCEPTUAL TEMPORAL DATA MODELING …… --- p.13 / Chapter 3.1 --- The Time Generic --- p.13 / Chapter 3.1.1 --- Time Unit --- p.14 / Chapter 3.1.2 --- Interval --- p.15 / Chapter 3.1.3 --- Periodic Time --- p.16 / Chapter 3.1.4 --- Time Point --- p.17 / Chapter 3.2 --- Extended Entity Relationship (EER) Model --- p.17 / Chapter 3.2.1 --- Attribute --- p.18 / Chapter 3.2.2 --- Entity --- p.22 / Chapter 3.2.3 --- Relationship --- p.23 / Chapter 3.2.4 --- Event --- p.23 / Chapter 3.3 --- EER Modeling of Temporal Data --- p.25 / Chapter 3.3.1 --- Classify entities and attributes --- p.25 / Chapter 3.3.2 --- Define events --- p.26 / Chapter 3.3.3 --- Define relationships --- p.27 / Chapter 3.3.4 --- Classify Attributes --- p.27 / Chapter CHAPTER 4 --- LOGICAL TEMPORAL DATABASE DESIGN --- p.28 / Chapter 4.1 --- Embedding a Temporal Relation into a Snapshot Relation --- p.28 / Chapter 4.2 --- The Proposed Temporal Relational Model --- p.29 / Chapter 4.2.1 --- Extension to Relational Model --- p.30 / Chapter 4.2.2 --- Extended Properties --- p.31 / Chapter 4.2.3 --- Extended Information Contents --- p.32 / Chapter 4.2.3.1 --- Different Temporal Information --- p.32 / Chapter 4.2.3.2 --- Retroactive and Postactive Recording --- p.33 / Chapter 4.2.3.3 --- Multiple Values at an Instant --- p.33 / Chapter 4.2.3.4 --- Discrete Valid Intervals --- p.34 / Chapter 4.2.4 --- Data Manipulation --- p.34 / Chapter 4.2.4.1 --- Retrieval --- p.35 / Chapter 4.2.4.2 --- Updating --- p.36 / Chapter 4.2.5 --- Probable Undesirable Properties --- p.37 / Chapter 4.2.5.1 --- Redundancy --- p.37 / Chapter 4.2.5.2 --- Update Anomalies --- p.39 / Chapter 4.2.5.3 --- Retrieval Anomalies --- p.39 / Chapter 4.3 --- Mapping Conceptual to Temporal Relational Model --- p.40 / Chapter 4.3.1 --- Relations For the Time Generic --- p.40 / Chapter 4.3.1.1 --- Interval --- p.40 / Chapter 4.3.1.2 --- Time Point --- p.41 / Chapter 4.3.1.3 --- Periodic Time --- p.42 / Chapter 4.3.2 --- Mapping History into Time Attributes --- p.42 / Chapter 4.3.2.1 --- Attribute History --- p.42 / Chapter 4.3.2.2 --- Existence History --- p.43 / Chapter 4.3.3 --- Mapping Entity Type --- p.43 / Chapter 4.3.3.1 --- Strong Entity --- p.43 / Chapter 4.3.3.2 --- Weak Entity --- p.46 / Chapter 4.3.3.3 --- Temporally Weak Entity --- p.46 / Chapter 4.3.4 --- Mapping Event Type --- p.46 / Chapter 4.3.5 --- Mapping Relationship Type --- p.48 / Chapter 4.4 --- Joining Synchronous Relations --- p.49 / Chapter 4.5 --- Integrity Constraints --- p.50 / Chapter 4.5.1 --- Creation --- p.51 / Chapter 4.5.2 --- Deletion --- p.51 / Chapter 4.5.3 --- Modification of Past States --- p.52 / Chapter CHAPTER 5 --- A TEMPORAL QUERY LANGUAGE - TempSQL --- p.53 / Chapter 5.1 --- New Statements --- p.53 / Chapter 5.2 --- New Constructs in Statements --- p.54 / Chapter 5.2.1 --- Temporal Operators --- p.54 / Chapter 5.2.2 --- Temporal Comparison Operators --- p.55 / Chapter 5.2.3 --- WHEN Clause --- p.56 / Chapter 5.2.4 --- AS OF clause --- p.58 / Chapter 5.2.5 --- VALID clause --- p.58 / Chapter 5.2.6 --- A General Example --- p.60 / Chapter 5.3 --- Semantics of TempSQL Statements --- p.61 / Chapter 5.3.1 --- SELECT --- p.62 / Chapter 5.3.2 --- INSERT --- p.64 / Chapter 5.3.3 --- DISCARD --- p.66 / Chapter 5.3.4 --- UPDATE --- p.68 / Chapter CHAPTER 6 --- IMPLEMENTATION OF TempSQL --- p.78 / Chapter 6.1 --- The Underlying Environment --- p.78 / Chapter 6.2 --- The Preprocessor --- p.79 / Chapter 6.3 --- The Interactive Interpreter --- p.81 / Chapter 6.4 --- Limitations --- p.82 / Chapter CHAPTER 7 --- AN EXAMPLE TEMPORAL DATABASE --- p.84 / Chapter 7.1 --- The Scenario --- p.84 / Chapter 7.2 --- EER modeling of data --- p.84 / Chapter 7.3 --- Transformation into Temporal Relations --- p.85 / Chapter 7.4 --- Joining Synchronous Relations --- p.87 / Chapter 7.5 --- Sample Queries --- p.87 / Chapter 7.6 --- Remarks --- p.91 / Chapter CHAPTER 8 --- CONCLUSION AND FUTURE RESEARCH / DIRECTIONS --- p.94 / Chapter APPENDIX A --- BNF of TempSQL --- p.100 / REFERENCES --- p.104