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 11 of 11 for “"Large-scale data analysis"”.
-
Scalable Data Transformations for Low-Latency Large-Scale Data Analysis
Interactive analysis of simulation results has become a mainstay of science and engineering. With continually increasing compute power, the size of simulation results continues to grow. However, network and mass storage device throughput are not increasing as quickly. This introduces difficulties …
-
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 …
-
Experimental and analytical techniques for studying mechanotransduction in articular cartilage
… of individual cells with the full tissue-scale response. Articular cartilage is one such system where the complex extracellular matrix and heterogeneous cell responses make it difficult to understand how chondrocytes respond to injury-inducing strain. In this thesis I will explore the …
-
Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines
… maintenance of wind energy assets. Manual analysis of wind turbine data is difficult and time-consuming due to its volume, variety, and, most importantly, the need for quick detection of issues. Machine learning (ML) methods are able to automate large-scale data analysis. However, the …
-
Novel Sequence-Based Method for Identifying Transcription Factor Binding Sites in Prokaryotic Genomes
… techniques for microbial genomic sequence analysis are becoming increasingly important. With next–generation sequencing technology and the human microbiome project underway, current sequencing capacity is significantly greater than the speed at which organisms of interest can be …
-
Centering Communities in Research and Technology Design
Large companies hold an increasingly large monopoly on data and information about community behavior, making contexts ranging from instrumented city neighborhoods to online platforms more legible than ever to private firms. Meanwhile, many communities lack the tools and methods they need to access, …
-
The Role of Identifications in Higher Education Decisions
… Chapter Four used two nationally representative datasets (Next Steps and NELS:88) and latent class analysis (LCA) to identify different classes of ‘qualified’ students who self-selected not to pursue HE: Work-Oriented Voluntary Leavers (WOVLs), HE Deniers, the ‘Uncertain group’ and the ‘Multiple …
-
Modelling biological networks : topology, dynamics and generation
… modelling, statistical simulation and large-scale data analysis. Our results indicated LRCs to be regulatory attenuators in GRNS, and were found to be absent from or rare in GRNs of E. coli K12, Mycobateria tuberculosis, yeasts and human non-cancer cells. However, they are enormous in …
-
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 …
-
Distributed Computing for Large-scale Graphs
… last decade has seen an increased attention on large-scale data analysis, caused mainly by the availability of new sources of data and the development of programming model that allowed their analysis. Since many of these sources can be modeled as graphs, many large-scale graph processing …
-
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 …