Since the late 90s, support and services at SAAB have produced and maintained a product called ELDIS. ELDIS is an application used by Swedish armed forces at air bases in Sweden and flight technicians at air bases. It displays electrical information, wire diagrams, and detailed information for cables, electrical equipment, and other electrical devices. The main problem for ELDIS is that when drawing wire diagrams in the application, it takes too long of a time when the stored procedures are retrieving information from the database. There are two significant areas in this project, analyzing and optimizing stored procedures and implementing a client-side solution. This project aims to guide SAAB to choose the right approach for solving the performance issue of the application and display some of the problems that can exist with slow stored procedures for companies in general. This project has optimized the most used stored procedure at SAAB and compared it to a client-side solution and the original application. The result of this project is that both the optimized stored procedure implementation and the client-side implementation is a faster option than the original implementation. It also highlights that when trying to optimize the stored procedures, indexing on the database should be considered for increasing the performance of a stored procedure.

Age, period, and cohort are three temporal dimensions that can make unique contributions to social and epidemiological changes that occur in populations over time. However, while the theoretical underpinnings for each temporal dimension are well established, the statistical techniques to assess the distinctive contributions of age, period and cohort are controversial. Unless questionable assumptions are imposed on the data, traditional linear regression models are incapable of estimating the independent contribution of each temporal dimension due to the linear dependence between age, period and cohort (A=P-C). Two recently developed methods, Hierarchical Age-PeriodCohort (HAPC) and Intrinsic Estimator (IE) models, enable researchers to estimate how all three temporal dimensions contribute to an outcome of interest without resorting to such assumptions. However, some simulation studies suggest that these new methods provide biased estimates of each temporal dimension. In this dissertation, I investigated whether practitioners can avoid biased results by first understanding the structure of the data. In Chapters 2 and 3, I examined whether visual plots of descriptive statistics and model selection statistics could identify various types of data structures through a series of simulation analyses. The results showed that preliminary data analysis is useful for identifying data structures that are compatible with the assumptions of HAPC and IE models. Moreover, when the data satisfied assumptions such as three-dimensionality and slight deviations from perfect functional forms, both HAPC and IE models tended to provide unbiased estimates of age, period and cohort effects. In Chapter 4, I provided a step-by-step demonstration for applying HAPC models by investigating the unique contributions of age, period and cohort to educational inequalities in the health of a large sample of U.S. adults. This study found that age and cohort effects contribute most to variability in health, and also that cross-validation is a useful way to incorporate HAPC models when preliminary analyses do not definitively show that the data structure is three dimensional.

The ADtree, a data structure useful for caching sufficient statistics, has been successfully adapted to grow lazily when memory is limited and to update sequentially with an incrementally updated dataset. However, even these modified forms of the ADtree still exhibit inefficiencies in terms of both space usage and query time, particularly on datasets with very high dimensionality and with high arity features. We propose five modifications to the ADtree, each of which can be used to improve size and query time under specific types of datasets and features. These modifications also provide an increased ability to precisely control how an ADtree is built and to tune its size given external memory or speed requirements.

<p>Synthesis of terrains with complex features has been a challenging problem in computer graphics since most of the existing methods are based on the height field representation. Complex features in terrains adds to the overall realism of the terrain. Hence, there is a need to synthesize terrains in real-time with complex features that adhere to user input. The methodology described in this thesis describes a novel way to synthesize terrains with complex features based on user drawn sketches. Layered stack data structure is used to ensure that the resulting terrain has complex features. Since, Neural Networks are used to generate the terrains, the process is real-time.</p>

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We report on the state of the field of algorithm visualization, both quantitatively and qualitatively. Computer science educators seem to find algorithm and data structure visualizations attractive for their classrooms. Educational research shows that some are effective while many are not. Clearly, then, visualizations are difficult to create and use right. There is little in the way of a supporting community, and many visualizations are downright poor. Topic distribution is heavily skewed towards simple concepts with advanced topics receiving little to no attention. We have cataloged nearly 400 visualizations available on the Internet. We have a wiki-based catalog which includes availability, platform, strengths and weaknesses, responsible personnel and institutions, and other data about each visualization. We have developed extraction and analysis tools to gather statistics about the corpus of visualizations. Based on analysis of this collection, we point out areas where improvements may be realized and suggest techniques for implementing such improvements. We pay particular attention to the free and open source software movement as a model which the visualization community may do well to emulate, from both a software engineering perspective and a community-building standpoint. / Master of Science

Water plays a critical role in every aspect of civilization: agriculture, industry, economy, environment, recreation, transportation, culture, and health. Much of America's drinking water and wastewater infrastructure; however, is old and deteriorating. A crisis looms as demands on these systems increase. The costs associated with renewal of these aging systems are staggering. There is a critical disconnect between the methodological remedies for infrastructure renewal problems and the current sequential or isolated manner of renewal analysis and execution. This points to the need for a holistic systems perspective to address the renewal problem. Therefore, new tools are needed to provide support for wastewater infrastructure decisions. Such decisions are necessary to sustain economic growth, environmental quality, and improved societal benefits. Accurate prediction of wastewater pipe structural and functional deterioration plays an essential role in asset management and capital improvement planning. The key to implementing an asset management strategy is a comprehensive understanding of asset condition, performance, and risk profile. The primary objective of this research is therefore to develop protocols and methods for evaluating the wastewater pipe performance. This research presents the life cycle of wastewater pipeline identifying the causes of pipe failure in different phases including design, manufacture, construction, operation and maintenance, and repair/rehabilitation/replacement. Various modes and mechanisms of pipe failure in wastewater pipes were identified for different pipe material which completed with results from extensive literature reviews, and interviews with utilities and pipe associations. After reviewing all relevant reports and utility databases, a set of standard pipe parameter list (data structure) and a pipe data collection methodology were developed. These parameters includes physical/structural, operational/functional, environmental and other parameters, for not only the pipe, but also the entire pipe system. This research presents a development of a performance index for wastewater pipes. The performance index evaluates each parameter and combines them mathematically through a weighted summation and a fuzzy inference system that reflects the importance of the various factors. The performance index were evaluated based on artificial data and field data to ensure that the index could be implemented to real scenarios. Developing a performance index led to the development of a probabilistic performance prediction model for wastewater pipes. A framework would enable effective and systematic wastewater pipe performance evaluation and prediction in asset management programs. / Ph. D.