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.
Results
Showing 1 to 20 of 41 for “"Data Space"”.
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Multimodal Data Space for Cooperative Intelligent Transport Systems
… and universal way to store and exchange data in such a traffic system. Furthermore, multimodal scenarios where different types of vehicles (e.g., cars and Unmanned Aerial System) interact with each other, are increasingly emerging. To overcome these challenges, this thesis presents a set …
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Enhancing computational notebooks with code+data space versioning
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01
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Creating emotionally aware performance environments: a phenomenological exploration of inferred and invisible data space
… of the performing body in physical and digital space. The choreographic and thematic context of the performance work has forced us', as makers, to ask questions about the nature of digital interactivity which in turn feeds the work theoretically, technically and thematically. A computer views …
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Distributed Linda: design, development, and characterization of the data subsystem
… model for concurrent processing employs a shared data space approach for interprocess communication. The development of the distributed Linda system presented in this thesis implements distributed process creation within the shared data space framework and provides for parallel execution of Linda …
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The eFacilitator : a meeting capture application and infrastructure
… can also create meetings which go into the same data space. The data space lets agents representing a meeting automatically find capture hardware to capture and index meetings without user intervention. The data space is enabled by a highly scalable distributed, object-oriented, semantic database.
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Use of live video overlay on 3D data for distributed collaborative review
… However, for sharing three-dimensional (3D) data, it is difficult to support such approaches of using hands and real ink in a seamless way. This thesis proposes a new system design called AnnoScape as an approach to performing a remote collaborative review of 3D digital data using a live …
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Static and Dynamic Human Analysis in Images and Videos
… We search for efficient ways to explore vast data space in multimodal and multimodel tracking and event detection scenarios. We also strive to find effective way to search for content-relevant material in large video databases.
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Structural Model Discovery in Temporal Event Data Streams
… (IRSM). More intelligent ways of conducting data analysis have been explored in recent years. Ma- chine learning and data mining systems that utilize pattern classification and discovery in non-textual data promise to bring new generations of powerful "crawlers" for knowledge discovery, e.g., …
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SYSTEMATIC METHODS FOR MEMORY ERROR DETECTION AND EXPLOITATION
… Then we look into new exploit mechanisms in the data space. We propose data-flow stitching, a novel method to connect disjoint data flows for severe attacks. Data-flow stitching significantly enlarges the capability of data-oriented attacks. Finally, we explore the expressiveness of data-oriented …
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Democratizing Details-on-demand Data Visualizations at Scale
… zoom to help the viewer navigate through a large data space. In the past years, we have witnessed an increasing amount of data visualization applications that embrace this paradigm to facilitate data exploration and analysis. Web maps are a clear example. However, due to the highly specialized …
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Shifting Paradigms: Data-Centric Approach for Marine Statics Correction using Symmetric Autoencoding
… of transformations between non-commensurate spaces: first, inversion from seismic data space to velocity model space and second, forward modeling from velocity model space to seismic data space. Statics correction within this paradigm has severe drawbacks, mainly the high compute, time and …
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The classification of acoustic emission signals via artificial neural network
… implemented. Based on the established orthogonal data space, feature extraction and data compression are accomplished through the training process. The reconstructed data, which is in orthogonal and compressed form with maximal variance, are used to improve the classification efficiency. Since the …
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Large-Scale Constraint-Based Pattern Mining
… pattern mining for three different data formats, item-set, sequence and graph, and focused on mining patterns of large sizes. Colossal patterns in each data formats are studied to discover pruning properties that are useful for direct mining of these patterns. For item-set data, we …
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Analyzing how grade four teachers plan to teach inquiry-based curriculum materials and the influences on their preparation of mathematics lessons
… that adopted the Investigations in Number, Data, & Space curriculum. Qualitative analysis of interview transcripts, surveys, and lesson plans was conducted using a framework to identify primary codes for processes and influences on teachers' planning.Results indicate that collaboration …
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Learning matrix and functional models in high-dimensions
… information from noisy high-dimensional data sets. A significant feature of such procedures is that the inferences made are statistically significant, computationally efficient and scientifically meaningful. In this thesis we make several contributions to such statistical procedures. Our …
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Contributions to modeling parasite dynamics and dimension reduction
… In the first project, we study a longitudinal data of infection with the parasite Giardia lamblia among children in Kenya. Understanding the infection and recovery rate from parasitic infections is valuable for public health planning. Two challenges in modeling these rates are (1) infection …
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Learning manifolds with the Parametrized Self-Organizing Map and Unsupervised Kernel Regression
… makes it possible to deal with noisy or missing data in a principled manner, and it facilitates the construction of PSOMs from data that are not organized in a grid topology. In the second part, the manifold learning algorithm Unsupervised Kernel Regression (UKR) is introduced as a counterpart to …
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Fast Clustering Using a Grid-Based Underlying Density Function Approximation
… learning task that seeks to partition a set of data into smaller groupings, referred to as “clusters”, where items within the same cluster are somehow alike, while differing from those in other clusters. There are many different algorithms for clustering, but many of them are overly complex and …
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Probabilistic methods for drug discovery
This thesis introduces a flexible visual data exploration framework which combines advanced projection algorithms from the machine learning domain with visual representation techniques developed in the information visualisation domain to help a user to explore and understand effectively large …
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DWCox: A density-weighted Cox model for outlier-robust prediction of prostate cancer survival
… Better performance on a specific data set may also be obtained by extensive manual exploration in the data space, but such developed models are subject to overfitting and usually not directly applicable to a different data set. We propose \model, a density-weighted Cox model that …
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