No description available.

No description available.

No description available.

Current trends indicate that the prevalence of ontologies will continue to increase within many domains. They are already commonly used to define controlled medical terminologies and form the backbone of the Semantic Web initiative. Very few tools that support versioning of ontologies are currently available, and those that provide difference detection and visualization are particularly lacking. We have implemented a tool called PROMPT-Viz that provides advanced visualizations using treemaps to help understand the location, impact, type and extent of changes that have occurred between versions on an ontology. PROMPT-Viz runs as a plug-in for the popular ProtCgC knowledge engineering environment and as such should be applicable to a large number of ontology developers.

Manning United States Army Reserve (USAR) units are fundamentally different from manning Regular Army (RA) units. A soldier assigned to a USAR unit must live within 75 miles or 90 minutes commute of his Reserve Center (RC). This makes reserve unit positioning a key factor in the ability to recruit to fill the unit. This thesis automates, documents, reconciles, and assembles data on over 30,000 ZIP Codes, over 800 RCs, and over 260 Military Occupational Specialties (MOSs), drawing on and integrating over a dozen disparate databases. This effort produces a single data file with demographic, vocational, and economic data on every ZIP Code in America, along with the six year results of its RA, USAR, sister service recruit production, and MOS suitability for each of the 264 MOSs. Preliminary model development accounts for about 70% recruit production variation by ZIP Code. This thesis also develops models for the top five MOSs to predict the maximum number of recruits obtained from a ZIP Code for that MOS. Examples illustrate that ZIP Codes vary in their ability to provide recruits with sufficient aptitude for technical fields. Two subsequent theses will use those results. One completes the MOS models. The second uses the models as constraints in an optimization model to position RCs. An initial version of the optimization model is developed in this thesis. Together, the three theses will provide a powerful tool for analysis of a strategic-based optimal reserve force stationing. / Lieutenant Colonel, United States Army

This thesis is concerned with the design and im plementation of a Relational Multimedia Database System, in short RMDBS. RMDBS is designed to efficiently use storage space and manipulate various kinds of data; attribute data, bit-m apped pictures, and program s in binary code. RMDBS is an integrated system which enables the user to manage and control operations on the different forms of data in a user friendly manner. This means that even nonexperienced users can work with the system. The work described in this thesis is novel in that a true multimedia database has been implemented within the framework of a traditional relational DBMS. Previous work in this area has concentrated either in building data base management systems for storing picture-based data or multimedia databases which are not true data base management systems. RMDBS is implemented using the Revelation data base management system.

Typescript, etc. / Digitized by Kansas Correctional Industries

Dai, Juan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 36-39). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Previous Work --- p.3 / Chapter 1.2 --- Proposed Framework --- p.5 / Chapter 1.3 --- Overview --- p.6 / Chapter 2 --- Placement of the Local Classifiers --- p.8 / Chapter 2.1 --- The Uncertainty Map --- p.9 / Chapter 2.2 --- Responsibility Mixture Model --- p.11 / Chapter 2.3 --- EM for Parameter Estimation --- p.12 / Chapter 2.3.1 --- E-Step --- p.14 / Chapter 2.3.2 --- M-Step --- p.15 / Chapter 2.3.3 --- Relationship with Gaussian Mixture Mod- els(GMM) --- p.16 / Chapter 3 --- Fusing of Locally Adaptive Classifiers --- p.18 / Chapter 3.1 --- Training --- p.18 / Chapter 3.2 --- Testing --- p.21 / Chapter 4 --- Algorithmic Characteristics --- p.23 / Chapter 4.1 --- Uncertainty Piloted Placement of Local Classifiers --- p.23 / Chapter 4.2 --- Uncertainty Piloted Fusing of Local Classifiers --- p.24 / Chapter 4.3 --- Related Work --- p.25 / Chapter 5 --- Experiments --- p.27 / Chapter 5.1 --- Dimensionality Reduction --- p.27 / Chapter 5.2 --- Two-Class Classification Problem: Gender Classification --- p.29 / Chapter 5.3 --- Multi-Class Classification: Face Recognition --- p.30 / Chapter 5.3.1 --- Varying the Lighting --- p.31 / Chapter 5.3.2 --- Varying the Pose --- p.32 / Chapter 5.3.3 --- Number of Features Extracted --- p.33 / Chapter 6 --- Conclusion --- p.34 / Bibliography --- p.36

Abstract not available

A materialized view is a derived data collecton stored in a database. When the source data for a materialized view is updated, the materialized view also needs to be updated. The process of updating a materialized view in response to changes in the source data is called view maintenance. There are two methods for maintaining a materialized view - recomputation and incremental computation. Recomputation computes the new view instance from scratch using the updated sources data. Incremental computation on the other hand, computes the new view instance by using the update to the source data, the old view instance, and possibly some source data. Incremental computation is widely accepted as a less expensive mathod of maintaining a view when the size of the update to the source data is small in relation to the size of the source data. / thesis (PhD)--University of South Australia, 2000