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 141 for “"Large-scale data"”.
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Large scale data analytics for resilience of energy networks
… with different characteristics. We conduct a large-scale study on recovery from 169 failure events at two operational distribution grids in the states of New York and Massachusetts. Guided by unsupervised learning from non-stationary data, our analysis finds that under the widely adopted …
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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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LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION
Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases, and gene expression profiles have been widely used in functional genomic studies. However, not much work in traditional gene expression profiling takes into account the …
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Algorithms for Large-scale Data Analytics and Applications to the COVID-19 Pandemic
… insights from an ever-increasing amount of data, and also important applications to apply our insights to the world. In this thesis, we demonstrate both sides of the coin. In the first part of the thesis, we focus on building scalable algorithms for large-scale data analytics. In Chapter 1, …
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Improving satellite data interoperability: Large-scale data integration and agricultural applications in the US Midwest
… However, accompanied with the surge of satellite data availability is the increasing burden of large-scale data manipulation and preparation. The motivation of this study is to make satellite data more usable for earth system modelers. This work features an interdisciplinary approach that …
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Large–scale data–driven network analysis of human–plasmodium falciparum interactome: extracting essential targets and processes for malaria drug discovery
… genome wide association study summary statistics data obtained from Gambia, Kenya and Malawi populations, Plasmodium falciparum selective pressure variants and functional datasets (protein sequences, interologs, host-pathogen intra-organism and host-pathogen inter-organism protein-protein …
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A scalable direct manipulation engine for position-aware presentational data management
With the explosion of data, large datasets become more common for data analysis. How- ever, existing analytic tools are lack of scalability and large-scale data management tools are lack of interactivity. A lot of data analysis tasks are based on the order of data, we are proposing the very first …
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Complex data analytics via sparse, low-rank matrix approximation
<p>Today, digital data is accumulated at a faster than ever speed in science, engineering, biomedicine, and real-world sensing. Data mining provides us an effective way for the exploration and analysis of hidden patterns from these data for a broad spectrum of applications. Usually, these datasets …
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Developing machine learning tools to understand transcriptional regulation in plants
… have led to the generation of much genomic data for the model plant, Arabidopsis. To understand gene responses activated by specific external stress signals, these large-scale data sets need to be analyzed to generate new insight of gene functions in stress responses. This poses new …
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Extending the relational model with constraint satisfaction
We propose a new approach to data driven constraint programming. By extending the relational model to handle constraints and variables as first class citizens, we are able to express first order logic SAT problems using an extended SQL which we refer to as SAT/SQL. With SAT/SQL, one can efficiently …
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Large-scale Geometric Data Decomposition, Processing and Structured Mesh Generation
… a fundamental and critical problem in geometric data modeling and processing. In most scientific and engineering tasks that involve numerical computations and simulations on 2D/3D regions or on curved geometric objects, discretizing or approximating the geometric data using a polygonal or …
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Data Clustering And Visualization Through Matrix Factorization
… groupings or clusters in multidimensional data based on perceived similarities among the patterns. The purpose of clustering is to extract useful information</p> <p>from unlabeled data.</p> <p>In order to present the extracted useful knowledge obtained by clustering in a meaningful way, …
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An Approach For Scalable First-Order Rule Learning On Twitter Data
… with graph-based modeling of social media data, to scale up first-order rule learning through Markov Logic Networks on a commodity cluster on large scale Twitter data. SRLearn takes advantage of distributed systems to partition large-scale data into smaller but meaningful partitions based …
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P2PHDFS: AN IMPLEMENTATION OF STATISTIC MULTIPLEXED COMPUTING ARCHITECTURE IN HADOOP FILE SYSTEM
… is designed to store and process extremely large-scale data sets reliably. This is a first attempt implementation of the Statistic Multiplexed Computing Architecture concept proposed by Dr. Shi for the existing Hadoop File System (HDFS) to eliminate all single point failures. Unlike HDFS, in …
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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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An Empty Promise? Digital Democracy in the Smart City
… On the other hand, they can be used for large-scale data collection and surveillance, posing a risk to the public sphere. This thesis investigates the impact of digitization on the legitimacy of democracy. It first develops a novel framework based on the theories of participatory and …
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Encouraging collaboration through a new data management approach
The ability to store large volumes of data is increasing faster than processing power. Some existing data management methods often result in data loss, inaccessibility or repetition of simulations. We propose a framework which promotes collaboration and simplifies data management. In particular we …
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Optimizing end-to-end machine learning pipelines for model training
Modern data analysis programs often consist of complex operations. They combine multiple heterogeneous data sources, perform data cleaning and feature transformations, and apply machine learning algorithms to train models on the preprocessed data. Existing systems can execute such end-to-end …
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Mold Allergomics: Comparative and Machine Learning Approaches
… are rare compared to non-allergens and thus the data is considered highly skewed. In order to achieve a confident set of predicted allergens from a genome, false positive rates must be lowered. Current allergen prediction tools often produce many false positives when applied to large-scale data …
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