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 318 for “"large data"”.
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Efficient replication of large data objects
… for improving the reliability and scalability of data services. The primary problem encountered in replication is the trade-off between amount of replication, performance, and consistency. A rule of thumb states that any replication algorithm must sacrifice at least one of these criteria. In this …
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Efficient structure search in large data sets
… there has been rapid increase in the amount of data being generated and published; therefore there is an emerging need for efficiently managing and processing large data sets. Among the existing problems, structure search is an important topic with many real applications, such as substructure …
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Global Energy Conservation in Large Data Networks
… and communications technology (ICT): the global data- and telecommunications network, the private and commercial datacenters it supports, and the 19 billion electronic devices around the globe it interconnects, through which we communicate, and access and produce information. As bandwidth and …
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Flexible and efficient computation in large data centres
… online computer applications rely on large-scale data analyses to offer personalised and improved products. These large-scale analyses are performed on distributed data processing execution engines that run on thousands of networked machines housed within an individual data centre. …
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Environment for Large Data Processing and Visualization Using MongoDB
Data means treasures to both scientists and business people. Scientists can discover significant rules and theories beneath data. People involved in business can find their potential customers and improvement suggestions from exploring data. To uncover these valuable treasures, we need effective …
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Speculative ray scheduling for large data visualization on supercomputers
… properties, but tracing rays through partitioned data to calculate global illumination is inefficient because of the I/O overhead incurred by rays migrating from one partition to another. For such data, ray scheduling methods have demonstrated improved rendering performance by amortizing costs …
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Multi-scale data sketching for large data analysis and visualization
<p>Analysis and visualization of large data sets is time consuming and sometimes can be a very difficult process, especially for 3D data sets. Therefore, data processing and visualization techniques have often been used in the case of different massive data analysis for efficiency and accuracy …
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Visual exploratory analysis of large data sets : evaluation and application
Large data sets are difficult to analyze. Visualization has been proposed to assist exploratory data analysis (EDA) as our visual systems can process signals in parallel to quickly detect patterns. Nonetheless, designing an effective visual analytic tool remains a challenge. This challenge is …
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Handling large data storage in synthesis of multiple FPGA systems
… FPGAs provide fast arithmetic operations and large enough internal RAM storage that makes them very appealing to prototyping large systems, even building DSP applications. So having a good architecture to begin with is a good asset to engineers. This thesis investigates the issues of handling …
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Data relocation and prefetching for programs with large data sets
… contain nested loop structures that process large arrays of data. The execution of these loop structures often produces memory preference patterns that poorly utilize data caches. Limited associativity and cache capacity result in cache conflict misses. Also, non-unit stride access patterns …
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Toward graph layout of large data visualization: algorithms, evaluations and application
Generating layouts for millions of points on a spatially-restricted platform is a difficult task with a unique set of constraints. These layouts are traditionally generated on a server out of sight of the user. User-oriented applications would benefit from a real-time view of layout generation, …
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Extending linear grouping analysis and robust estimators for very large data sets
Cluster analysis is the study of how to partition data into homogeneous subsets so that the partitioned data share some common characteristic. In one to three dimensions, the human eye can distinguish well between clusters of data if clearly separated. However, when there are more than three …
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Modifications To The Fuzzy-ARTMAP Algorithm For Distributed Learning In Large Data Sets
… of thousands. In this dissertation we apply data partitioning and network partitioning to the FAM algorithm in a sequential and parallel setting to achieve better convergence time and to efficiently train with large databases (hundreds of thousands of patterns). We implement our …
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Specifications and design of a flexible information management system for large data bases.
Massachusetts Institute of Technology. Dept. of Ocean Engineering. Thesis. 1974. M.S.
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Measuring the accuracy of four attributes of sound for conveying changes in a large data set.
… generated would changed based on the underlying data value at any given point. An experiment was conducted to determine which attribute of sound most accurately represents data values in an auditory display. The four attributes of sound tested were frequency-sine waveform, frequency-sawtooth …
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USING PRINCIPAL COMPONENT ANALYSIS (PCA) TO OBTAIN AUXILIARY VARIABLES FOR MISSING DATA IN LARGE DATA SETS
… an important issue in the imputation of missing data in large data sets. The issue can arise in any analysis in which auxiliary variables are used to inform a modern missing data handling procedure (e.g., FIML, MI) to support the missing at random assumption, reduce bias and decrease standard …
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Comparison study on sorting techniques in static data structure
To manage and organize large data is imperative in order to formulate the data analysis and data processing efficiency. Thus, to handle large data becomes highly enviable, whilst, it is premised that the sorting techniques eliminate ambiguities with less effort. Therefore, this study investigates …
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Contributions to Bayesian inference via spectral methods
… is the computational burden, especially for large data. Using ideas from Fourier analysis, the original signal (data) domain can be transformed into the frequency domain, which portrays how the signal is decomposed across different frequencies, which is known as the spectrum. A key property …
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Data mining and database systems: integrating conceptual clustering with a relational database management system.
… and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, …
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Damage and repair identification in reinforced concrete beams modelled with various damage scenarios using vibration data
… that can efficiently be applied to real-time large data for detection, classification, localisation and quantification of the potential structural damage.
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