Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"Data Transformations"”.
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Scalable Data Transformations for Low-Latency Large-Scale Data Analysis
… moving elements strongly dependent on the input data size from the interactive phase of the workflow to the data preparation phase. This reduces the overall computational complexity of the interactive phase, enabling reduced interaction latency. Two related groups of approaches are explored: …
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A Comparative Study of Data Transformations for Efficient XML and JSON Data Compression. An In-Depth Analysis of Data Transformation Techniques, including Tag and Capital Conversions, Character and Word N-Gram Transformations, and Domain-Specific Data Transforms using SMILES Data as a Case Study
XML is a widely used data exchange format. The verbose nature of XML leads to the requirement to efficiently store and process this type of data using compression. Various general-purpose transforms and compression techniques exist that can be used to transform and compress XML data. More compact …
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Methodological focus and application of exploratory dietary patterns in epidemiological research
… is a useful method that utilizes multivariate data reduction techniques to summarize overall dietary exposure into variables that can represent diet in observational studies. This dissertation presents two projects that focus on methodological considerations for the exploratory approach to …
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An Empirical Comparison of Four Data Generating Procedures in Parametric and Nonparametric ANOVA
… the Type I error and power rates of four data transformations that produce a variety of non-normal distributions. Specifically, the transformations investigated were (a) the g-and-h, (b) the generalized lambda distribution (GLD), (c) the power method, and (d) the Burr families of …
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Quantization of multiresolution transform coefficients for high compression of digital images
… methods that perform transformation of the data. The digital image data transformations include quadrature mirror filtering, conjugate quadrature filtering, and wavelet methods. The process of transformation may be implemented in a reversible manner such that no change in the data is …
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SigPro: Enabling Subject Matter Expert Guidance in Feature Engineering
… Experts (SMEs). SigPro includes a suite of data processing building blocks, or primitives, as well as an algorithm to combine primitives to form feature engineering pipelines. These pipelines are in turn used to construct features for machine learning. SMEs, through a low-code interface, …
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Threat Detection in Program Execution and Data Movement: Theory and Practice
… threats. They compromise the confidentiality of data, the integrity of program logic, and the availability of services. This threat becomes even severer when followed by other malicious activities such as data exfiltration. The integration of primitive attacks constructs comprehensive attack …
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Identifying system-wide contact center cost reduction opportunities through lean, customer-focused IT metrics
… customers. Yet, no such customer-centric data framework exists at Dell, or indeed in the contact center industry. However, it is possible to create just such a customer focused data framework by applying an automated value stream mapping (VSM) analysis to a large sample of contact-center …
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New Covariance-Based Feature Extraction Methods for Classification and Prediction of High-Dimensional Data
<p>When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional …
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Synthesizing tabular data using conditional GAN
In data science, the ability to model the distribution of rows in tabular data and generate realistic synthetic data enables various important applications including data compression, data disclosure, and privacy-preserving machine learning. However, because tabular data usually contains a mix of …
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Medication recommendations vs. peer practice in pediatric levothyroxine dosing : a study of collective intelligence from a clinical data warehouse as a potential model for clinical decision support
… in medication dosing patterns. Patient clinical data warehouses (CDW) may be able to bridge the knowledge gap. CDWs contain the collective intelligence of various contributors (i.e. clinicians, administrators, etc.) where each data entry provides information regarding medical care for a patient …
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Dynamic Workflows and Advanced Data Management for Problem Solving Environments
… is an emerging topic that aims to combine both data-oriented and execution-oriented views of scientific experiments, and closely integrate the processes underlying the practice of computational science with the software artifacts constituted by the PSE. This thesis presents a workflow management …
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Currency risk and imperfect knowledge: Cointegrated VAR analyses with survey data
… exchange market through the use of survey data on traders' exchange rate forecasts. On the whole, this literature, which is reviewed in chapter 1, has found that excess returns derive from both violations of the rational expectations hypothesis (non white-noise forecast errors) as well as a …
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SUNPRISM: A Software Framework for Climate Change Research
… investigation via new capabilities for combining data transformations, model simulations, and output visualizations in application scenarios developed for climate change research. Consisting of two specialized software tools, SUNPRISM Scenario Manager and SUNPRISM Visualizer, the SUNPRISM …
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Building Reliable AI under Distribution Shifts
… between training and deployment data—can significantly impact their reliability. These shifts affect models in multiple ways, leading to degraded generalization, fairness collapse, loss of robustness, and new safety vulnerabilities. This dissertation investigates how to build …
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Data-Intensive Biocomputing in the Cloud
… However, these NGS technologies generate data at a rate that far outstrips Moore\'s Law. As a consequence, analyzing this exponentially increasing data deluge requires enormous computational and storage resources, resources that many life science institutions do not have access to. As …
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Methodology to Validate Traffic Speed Deflection Devices (TSDDs) Measurements Using Laser Doppler Vibrometers (LDV) Sensors
… way to collect pavement structural condition data at the network-level. However, the integration of TSDD measurements into pavement management and design applications remains limited due to the lack of standardized data validity procedures. This research proposes a methodology to verify TSDs …
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Transforming and Optimizing Irregular Applications for Parallel Architectures
… many approaches have been proposed to improve data locality and reduce irregularities through computational and data transformations. However, there are two major drawbacks in these existing approaches that prevent them from achieving optimal performance. First, these approaches use local …
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Optimal spectral reconstructions from deterministic and stochastic sampling geometries using compressive sensing and spectral statistical models
… the aliasing effects of under-sampled Fourier data. The proposed solution utilizes an additional layer of optimization to enhance the performance of a previously published CS reconstruction algorithm. Specifically, the new framework provides reconstructions of a desired image quality by jointly …
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Conservation of polychaete biodiversity within the Port Stephens-Great Lakes Marine Park
… species composition was greater in the long-term data set. Site rankings in successive sampling periods for species richness were uncorrelated in the short-term data set, and correlated in the long-term data set. Site rankings in successive sampling periods for total abundance were uncorrelated in …