This thesis analyzed the principles and concepts of Marine Aviation Logistics doctrine at the tactical level and the current Information Management Systems used to execute mission requirements. A web-enabled prototype for Marine Aviation Logistics Squadrons (MALS) was developed to optimize management and decision support for deliberate, time sensitive and crisis action planning of aviation support operations. The first iteration of the prototype was tested by two Operations (S-3) Officers formerly assigned to active-duty Marine Aviation Logistics Squadrons (MALS). The application was also subjected to a usability experiment at the Database and Web Technologies Lab at the Naval Postgraduate School. The results of this research revealed potential benefits for tactical-level aviation logistics planners and sustainers; the prototype is a viable concept, worthy of future development.

This thesis analyzes the career paths of U.S. Army officers and evaluates the effect of commissioning source on their survival patterns. Data used in this study are taken from the Active Duty Military Master File provided by the Defense Manpower Data Center (DMDC). The data set contains information on 103,501 officers who were commissioned between 1981 and 2001. The results indicate that commissioning source, occupation (except for the special occupations and military police) and occupation category have significant effects on the survival curves of U.S. Army officers. Officers graduating from the ROTC Scholarship program and commissioned through Direct Commissioning have 10% and 19% greater hazards of leaving than USMA graduates; officers graduating from ROTC Non-Scholarship and OCS have 6% and 8% lower hazards of leaving than USMA graduates. Age, race and ethnicity, gender, marital status, number of non-spousal dependents, and graduate education all have significant effects on the survival function. Higher age at commissioning, being African-American, being married, each additional non-spousal dependent, and having a graduate degree have positive effects on survival patterns while being female has a negative effect on the survival patterns. Being prior enlisted is not statistically significant in all of the models, but when it is significant, it has a positive effect on the survival function.

RA requested that these models be examined in an effort to combine the functionality of each. This thesis builds a model that does just that. The fundamental concept of the model involves taking the current inventory of Marines (by military occupational specialty [MOS] and grade) and applying transition rates to each of them in order to determine how many are in what state at the end of the upcoming year. The necessary number of reenlistments is then calculated by subtracting the forecasted inventory from a desired force structure known as the Grade Adjusted Recapitulation. Manpower planners can use the results of this model to establish the number of boat spaces for each of the first-term MOSs as well as recommended reenlistment goals for the subsequent-term MOSs.

e appears to be significant potential for improving climate forecasts for SWA. The DoD still relies on long-term means to create climatological planning products to the field. We feel that by incorporating the anomalies associated with the climate variations discussed here, the DoD could add significant value to its climatology products.

Approved for public release; distribution in unlimited. / The relational database model has become the most popular and widespread database model. Most current database systems are based upon or related to -he relational model. However, the relational model is beset with significant limitations, pitfalls and deficiencies. The relational model can be substantially improved with graphical interfaces. To this end, the Graphics Language for Accessing Database (GLAD) can provide easy to use and learn graphics interfaces for the relational model. Data structures and algorithms for GLAD will be presented to extend the relational model. / http://archive.org/details/datastructuresal00gren / Captain, United States Marine Corps

The rate of data growth outpaces the decline of hardware costs, and there has been an ever-increasing demand in reducing the storage and network overhead for online database management systems (DBMSs). The most widely used approach for data reduction in DBMSs is blocklevel compression. Although this method is simple and effective, it fails to address redundancy across blocks and therefore leaves significant room for improvement for many applications. This dissertation proposes a systematic approach, termed similaritybased deduplication, which reduces the amount of data stored on disk and transmitted over the network beyond the benefits provided by traditional compression schemes. To demonstrate the approach, we designed and implemented dbDedup, a lightweight record-level similaritybased deduplication engine for online DBMSs. The design of dbDedup exploits key observations we find in database workloads, including small item sizes, temporal locality, and the incremental nature of record updates. The proposed approach differs from traditional chunk-based deduplication approaches in that, instead of finding identical chunks anywhere else in the data corpus, similarity-based deduplication identifies a single similar data-item and performs differential compression to remove the redundant parts for greater savings. To achieve high efficiency, dbDedup introduces novel encoding, caching and similarity selection techniques that significantly mitigate the deduplication overhead with minimal loss of compression ratio. For evaluation, we integrated dbDedup into the storage and replication components of a distributed NoSQL DBMS and analyzed its properties using four real datasets. Our results show that dbDedup achieves up to 37⇥ reduction in the storage size and replication traffic of the database on its own and up to 61⇥ reduction when paired with the DBMS’s block-level compression. dbDedup provides both benefits with negligible effect on DBMS throughput or client latency (average and tail).

King-Fung Iu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 72-74).

Kam, Po-shan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 89-97). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgements --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining --- p.1 / Chapter 1.2 --- Temporal Data Management --- p.2 / Chapter 1.3 --- Temporal reasoning and temporal semantics --- p.3 / Chapter 1.4 --- Temporal Data Mining --- p.5 / Chapter 1.5 --- Motivation --- p.6 / Chapter 1.6 --- Approach --- p.7 / Chapter 1.6.1 --- Focus and Objectives --- p.8 / Chapter 1.6.2 --- Experimental Setup --- p.8 / Chapter 1.7 --- Outline and contributions --- p.9 / Chapter 2 --- Relevant Work --- p.10 / Chapter 2.1 --- Data Mining --- p.10 / Chapter 2.1.1 --- Association Rules --- p.13 / Chapter 2.1.2 --- Classification --- p.15 / Chapter 2.1.3 --- Clustering --- p.16 / Chapter 2.2 --- Sequential Pattern --- p.17 / Chapter 2.2.1 --- Frequent Patterns --- p.18 / Chapter 2.2.2 --- Interesting Patterns --- p.20 / Chapter 2.2.3 --- Granularity --- p.21 / Chapter 2.3 --- Temporal Database --- p.21 / Chapter 2.4 --- Temporal Reasoning --- p.23 / Chapter 2.4.1 --- Natural Language Expression --- p.24 / Chapter 2.4.2 --- Temporal Logic Approach --- p.25 / Chapter 2.5 --- Temporal Data Mining --- p.25 / Chapter 2.5.1 --- Framework --- p.25 / Chapter 2.5.2 --- Temporal Association Rules --- p.26 / Chapter 2.5.3 --- Attribute-Oriented Induction --- p.27 / Chapter 2.5.4 --- Time Series Analysis --- p.27 / Chapter 3 --- Discovering Temporal Patterns for interval-based events --- p.29 / Chapter 3.1 --- Temporal Database --- p.29 / Chapter 3.2 --- Allen's Taxonomy of Temporal Relationships --- p.31 / Chapter 3.3 --- "Mining Temporal Pattern, AppSeq and LinkSeq" --- p.33 / Chapter 3.3.1 --- A1 and A2 temporal pattern --- p.33 / Chapter 3.3.2 --- "Second Temporal Pattern, LinkSeq" --- p.34 / Chapter 3.4 --- Overview of the Framework --- p.35 / Chapter 3.4.1 --- "Mining Temporal Pattern I, AppSeq" --- p.36 / Chapter 3.4.2 --- "Mining Temporal Pattern II, LinkSeq" --- p.36 / Chapter 3.5 --- Summary --- p.37 / Chapter 4 --- "Mining Temporal Pattern I, AppSeq" --- p.38 / Chapter 4.1 --- Problem Statement --- p.38 / Chapter 4.2 --- Mining A1 Temporal Patterns --- p.40 / Chapter 4.2.1 --- Candidate Generation --- p.43 / Chapter 4.2.2 --- Large k-Items Generation --- p.46 / Chapter 4.3 --- Mining A2 Temporal Patterns --- p.48 / Chapter 4.3.1 --- Candidate Generation: --- p.49 / Chapter 4.3.2 --- Generating Large 2k-Items: --- p.51 / Chapter 4.4 --- Modified AppOne and AppTwo --- p.51 / Chapter 4.5 --- Performance Study --- p.53 / Chapter 4.5.1 --- Experimental Setup --- p.53 / Chapter 4.5.2 --- Experimental Results --- p.54 / Chapter 4.5.3 --- Medical Data --- p.58 / Chapter 4.6 --- Summary --- p.60 / Chapter 5 --- "Mining Temporal Pattern II, LinkSeq" --- p.62 / Chapter 5.1 --- Problem Statement --- p.62 / Chapter 5.2 --- "First Method for Mining LinkSeq, LinkApp" --- p.63 / Chapter 5.3 --- "Second Method for Mining LinkSeq, LinkTwo" --- p.64 / Chapter 5.4 --- "Alternative Method for Mining LinkSeq, LinkTree" --- p.65 / Chapter 5.4.1 --- Sequence Tree: Design --- p.65 / Chapter 5.4.2 --- Construction of seq-tree --- p.69 / Chapter 5.4.3 --- Mining LinkSeq using seq-tree --- p.76 / Chapter 5.5 --- Performance Study --- p.82 / Chapter 5.6 --- Discussions --- p.85 / Chapter 5.7 --- Summary --- p.85 / Chapter 6 --- Conclusion and Future Work --- p.87 / Chapter 6.1 --- Conclusion --- p.87 / Chapter 6.2 --- Future Work --- p.88 / Bibliography --- p.97

Yu Xiaohui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 98-115). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.6 / Chapter 2.1 --- Query Optimization --- p.6 / Chapter 2.2 --- Query Rewriting --- p.8 / Chapter 2.2.1 --- Optimizing Multi-Block Queries --- p.8 / Chapter 2.2.2 --- Semantic Query Optimization --- p.13 / Chapter 2.2.3 --- Query Rewriting in Starburst --- p.15 / Chapter 2.3 --- Plan Generation --- p.16 / Chapter 2.3.1 --- Dynamic Programming Approach --- p.16 / Chapter 2.3.2 --- Join Query Processing --- p.17 / Chapter 2.3.3 --- Queries with Aggregates --- p.23 / Chapter 2.4 --- Statistics and Cost Estimation --- p.24 / Chapter 2.5 --- Histogram Techniques --- p.27 / Chapter 2.5.1 --- Definitions --- p.28 / Chapter 2.5.2 --- Trivial Histograms --- p.29 / Chapter 2.5.3 --- Heuristic-based Histograms --- p.29 / Chapter 2.5.4 --- V-Optimal Histograms --- p.32 / Chapter 2.5.5 --- Wavelet-based Histograms --- p.35 / Chapter 2.5.6 --- Multidimensional Histograms --- p.35 / Chapter 2.5.7 --- Global Histograms --- p.37 / Chapter 3 --- New Histogram Techniques --- p.39 / Chapter 3.1 --- Piecewise Linear Histograms --- p.39 / Chapter 3.1.1 --- Construction --- p.41 / Chapter 3.1.2 --- Usage --- p.43 / Chapter 3.1.3 --- Error Measures --- p.43 / Chapter 3.1.4 --- Experiments --- p.45 / Chapter 3.1.5 --- Conclusion --- p.51 / Chapter 3.2 --- A-Optimal Histograms --- p.54 / Chapter 3.2.1 --- A-Optimal(mean) Histograms --- p.56 / Chapter 3.2.2 --- A-Optimal(median) Histograms --- p.58 / Chapter 3.2.3 --- A-Optimal(median-cf) Histograms --- p.59 / Chapter 3.2.4 --- Experiments --- p.60 / Chapter 4 --- Global Histograms --- p.64 / Chapter 4.1 --- Wavelet-based Global Histograms --- p.65 / Chapter 4.1.1 --- Wavelet-based Global Histograms I --- p.66 / Chapter 4.1.2 --- Wavelet-based Global Histograms II --- p.68 / Chapter 4.2 --- Piecewise Linear Global Histograms --- p.70 / Chapter 4.3 --- A-Optimal Global Histograms --- p.72 / Chapter 4.3.1 --- Experiments --- p.74 / Chapter 5 --- Dynamic Maintenance --- p.81 / Chapter 5.1 --- Problem Definition --- p.83 / Chapter 5.2 --- Refining Bucket Coefficients --- p.84 / Chapter 5.3 --- Restructuring --- p.86 / Chapter 5.4 --- Experiments --- p.91 / Chapter 6 --- Conclusions --- p.95 / Bibliography --- p.98

by Hwang Hoi Yee Cothan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 68-70). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Objectives of the Thesis --- p.1 / Chapter 1.2 --- Overview of the Thesis --- p.7 / Chapter 2 --- Background and Related Work --- p.8 / Chapter 2.1 --- Deductive Databases --- p.8 / Chapter 2.2 --- Disjunctive Deductive Databases --- p.10 / Chapter 2.3 --- Model tree for disjunctive deductive databases --- p.11 / Chapter 3 --- Preliminary --- p.13 / Chapter 3.1 --- Disjunctive Logic Program --- p.13 / Chapter 3.2 --- Data-disjunctive Logic Program --- p.14 / Chapter 4 --- Semantics of Data-disjunctive Logic Program --- p.17 / Chapter 4.1 --- Model-theoretic semantics --- p.17 / Chapter 4.2 --- Fixpoint semantics --- p.20 / Chapter 4.2.1 --- Fixpoint operators corresponding to the MMSpDD --- p.22 / Chapter 4.2.2 --- "Fixpoint operator corresponding to the contingency model, CMP" --- p.25 / Chapter 4.3 --- Equivalence between the model-theoretic and fixpoint semantics --- p.26 / Chapter 4.4 --- Operational Semantics --- p.30 / Chapter 4.5 --- Correspondence with the I-table --- p.31 / Chapter 5 --- Disjunctive Deductive Databases --- p.33 / Chapter 5.1 --- Disjunctions in deductive databases --- p.33 / Chapter 5.2 --- Relation between predicates --- p.35 / Chapter 5.3 --- Transformation of Disjunctive Deductive Data-bases --- p.38 / Chapter 5.4 --- Query answering for Disjunctive Deductive Data-bases --- p.40 / Chapter 6 --- Magic for Data-disjunctive Deductive Database --- p.44 / Chapter 6.1 --- Magic for Relevant Answer Set --- p.44 / Chapter 6.1.1 --- Rule rewriting algorithm --- p.46 / Chapter 6.1.2 --- Bottom-up evaluation --- p.49 / Chapter 6.1.3 --- Examples --- p.49 / Chapter 6.1.4 --- Discussion on the rewriting algorithm --- p.52 / Chapter 6.2 --- Alternative algorithm for Traditional Answer Set --- p.54 / Chapter 6.2.1 --- Rule rewriting algorithm --- p.54 / Chapter 6.2.2 --- Examples --- p.55 / Chapter 6.3 --- Contingency answer set --- p.56 / Chapter 7 --- Experiments and Comparison --- p.57 / Chapter 7.1 --- Experimental Results --- p.57 / Chapter 7.1.1 --- Results for the Traditional answer set --- p.58 / Chapter 7.1.2 --- Results for the Relevant answer set --- p.61 / Chapter 7.2 --- Comparison with the evaluation method for Model tree --- p.63 / Chapter 8 --- Conclusions and Future Work --- p.66 / Bibliography --- p.68