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Dissertations / Theses on the topic 'High volume big data'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Le, Montagner Roman. "High-Energy Transient Universe in the Era of Large Optical Surveys." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP089.

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L'astronomie multi-messagers combine des données de sources variées comme les photons, les ondes gravitationnelles (OG), les neutrinos et les rayons cosmiques. Des avancées significatives ont été réalisées en 1987 avec la détection de neutrinos d'une supernova proche et en 2017 avec la détection conjointe d'OG, d'un sursaut gamma court et d'une kilonova provenant d'une fusion de deux étoiles à neutrons. Ce domaine devrait croître avec le lancement de nouveaux observatoires tels que SVOM, le télescope Einstein, Icecube, KM3Net et l'observatoire Vera C. Rubin. Le Legacy Survey of Space and Time

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2

Danesh, Sabri. "BIG DATA : From hype to reality." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-37493.

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Big data is all of a sudden everywhere. It is too big to ignore!It has been six decades since the computer revolution, four decades after the development of the microchip, and two decades of the modern Internet! More than a decade after the 90s “.com” fizz, can Big Data be the next Big Bang? Big data reveals part of our daily lives. It has the potential to solve virtually any problem for a better urbanized global. Big Data sources are also very interesting from an official statistics point of view. The purpose of this paper is to explore the conceptions of big data and opportunities and challe

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Tudoran, Radu-Marius. "High-Performance Big Data Management Across Cloud Data Centers." Electronic Thesis or Diss., Rennes, École normale supérieure, 2014. http://www.theses.fr/2014ENSR0004.

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La puissance de calcul facilement accessible offerte par les infrastructures clouds, couplés à la révolution du "Big Data", augmentent l'échelle et la vitesse auxquelles l'analyse des données est effectuée. Les ressources de cloud computing pour le calcul et le stockage sont répartis entre plusieurs centres de données de par le monde. Permettre des transferts de données rapides devient particulièrement important dans le cadre d'applications scientifiques pour lesquels déplacer le traitement proche de données est coûteux voire impossible. Les principaux objectifs de cette thèse consistent à ana

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Tran, Viet-Trung. "Scalable data-management systems for Big Data." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00920432.

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Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data manage

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5

Zhang, Liangwei. "Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17012.

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Big Data analytics has attracted intense interest from both academia and industry recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional data streams are being collected and curated by enterprises to support their decision-making. Fault detection from these data is one of the important applications in eMaintenance solutions with the aim of supporting maintenance decision-making. Early discovery of system faults may ensure the reliabi

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Griffin, Alan R., and R. Stephen Wooten. "AUTOMATED DATA MANAGEMENT IN A HIGH-VOLUME TELEMETRY DATA PROCESSING ENVIRONMENT." International Foundation for Telemetering, 1992. http://hdl.handle.net/10150/608908.

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International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California<br>The vast amount of data telemetered from space probe experiments requires careful management and tracking from initial receipt through acquisition, archiving, and distribution. This paper presents the automated system used at the Phillips Laboratory, Geophysics Directorate, for tracking telemetry data from its receipt at the facility to its distribution on various media to the research community. Features of the system include computerized databa

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7

Lu, Feng. "Big data scalability for high throughput processing and analysis of vehicle engineering data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207084.

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"Sympathy for Data" is a platform that is utilized for Big Data automation analytics. It is based on visual interface and workflow configurations. The main purpose of the platform is to reuse parts of code for structured analysis of vehicle engineering data. However, there are some performance issues on a single machine for processing a large amount of data in Sympathy for Data. There are also disk and CPU IO intensive issues when the data is oversized and the platform need fits comfortably in memory. In addition, for data over the TB or PB level, the Sympathy for data needs separate functiona

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Tang, Yuzhe. "Secure and high-performance big-data systems in the cloud." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53995.

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Cloud computing and big data technology continue to revolutionize how computing and data analysis are delivered today and in the future. To store and process the fast-changing big data, various scalable systems (e.g. key-value stores and MapReduce) have recently emerged in industry. However, there is a huge gap between what these open-source software systems can offer and what the real-world applications demand. First, scalable key-value stores are designed for simple data access methods, which limit their use in advanced database applications. Second, existing systems in the cloud need automa

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Abidi, Faiz Abbas. "Remote High Performance Visualization of Big Data for Immersive Science." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78210.

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Remote visualization has emerged as a necessary tool in the analysis of big data. High-performance computing clusters can provide several benefits in scaling to larger data sizes, from parallel file systems to larger RAM profiles to parallel computation among many CPUs and GPUs. For scalable data visualization, remote visualization tools and infrastructure is critical where only pixels and interaction events are sent over the network instead of the data. In this paper, we present our pipeline using VirtualGL, TurboVNC, and ParaView to render over 40 million points using remote HPC clusters and

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Mercier, Michael. "Contribution to High Performance Computing and Big Data Infrastructure Convergence." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAM031/document.

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La quantité de données produites dans le monde scientifique comme dans le monde commercial, est en constante augmentation. Le domaine du traitement de donnée à large échelle, appelé “Big Data”, a été inventé pour traiter des données sur de larges infrastructures informatiques distribuées. Mais l’intégration de système Big Data sur des machines de calcul intensif pose de nombreux problèmes. En effet, les gestionnaires de ressources ainsi que les systèmes de fichier de super calculateurs ne sont pas penser pour ce type de travail. Le sujet de cette thèse est de trouver la meilleure approche pour

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Cao, Hongfei. "High-throughput Visual Knowledge Analysis and Retrieval in Big Data Ecosystems." Thesis, University of Missouri - Columbia, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877134.

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<p> Visual knowledge plays an important role in many highly skilled applications, such as medical diagnosis, geospatial image analysis and pathology diagnosis. Medical practitioners are able to interpret and reason about diagnostic images based on not only primitive-level image features such as color, texture, and spatial distribution but also their experience and tacit knowledge which are seldom articulated explicitly. This reasoning process is dynamic and closely related to real-time human cognition. Due to a lack of visual knowledge management and sharing tools, it is difficult to capture a

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Zeng, Yaohui. "Scalable sparse machine learning methods for big data." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/6021.

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Sparse machine learning models have become increasingly popular in analyzing high-dimensional data. With the evolving era of Big Data, ultrahigh-dimensional, large-scale data sets are constantly collected in many areas such as genetics, genomics, biomedical imaging, social media analysis, and high-frequency finance. Mining valuable information efficiently from these massive data sets requires not only novel statistical models but also advanced computational techniques. This thesis focuses on the development of scalable sparse machine learning methods to facilitate Big Data analytics. Built upo

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Su, Yu. "Big Data Management Framework based on Virtualization and Bitmap Data Summarization." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420738636.

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Bhih, A. "High performance decentralised community detection algorithms for big data from smart communication applications." Thesis, Liverpool John Moores University, 2018. http://researchonline.ljmu.ac.uk/8399/.

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Many systems in the world can be represented as models of complex networks and subsequently be analysed fruitfully. One fundamental property of the real-world networks is that they usually exhibit inhomogeneity in which the network tends to organise according to an underlying modular structure, commonly referred to as community structure or clustering. Analysing such communities in large networks can help people better understand the structural makeup of the networks. For example, it can be used in mobile ad-hoc and sensor networks to improve the energy consumption and communication tasks. Thu

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Myers, Julius (Julius Scott). "Implementing postponement into low-volume/high-variability manufacturing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111535.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.<br>Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, in conjunction with the Leaders for Global Operations Program at MIT, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 60-61).<br>Aircraft Company X (AX) manufactures and assembles an immense variety of parts utilized as drive systems and rotor componen

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Kouzoupis, Antonios. "High performance shared state schedulers." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-196145.

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Large organizations and research institutes store a huge volume of data nowadays.In order to gain any valuable insights distributed processing frameworks over acluster of computers are needed. Apache Hadoop is the prominent framework fordistributed storage and data processing. At SICS Swedish ICT we are building Hops, a new distribution of Apache Hadoop relying on a distributed, highly available MySQL Cluster NDB to improve performance. Hops-YARN is the resource management framework of Hops which introduces distributed resource management, load balancing the tracking of resources in a cluster.

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Zhang, Liangwei. "Big Data Analytics for Fault Detection and its Application in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60423.

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Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems a

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18

Sweeney, Michael John. "A framework for scoring and tagging NetFlow data." Thesis, Rhodes University, 2019. http://hdl.handle.net/10962/65022.

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With the increase in link speeds and the growth of the Internet, the volume of NetFlow data generated has increased significantly over time and processing these volumes has become a challenge, more specifically a Big Data challenge. With the advent of technologies and architectures designed to handle Big Data volumes, researchers have investigated their application to the processing of NetFlow data. This work builds on prior work wherein a scoring methodology was proposed for identifying anomalies in NetFlow by proposing and implementing a system that allows for automatic, real-time scoring th

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19

Cho, Jang Ik. "Partial EM Procedure for Big-Data Linear Mixed Effects Model, and Generalized PPE for High-Dimensional Data in Julia." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case152845439167999.

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20

Dash, Sajal. "Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99798.

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Experimental and observational data emerging from various scientific domains necessitate fast, accurate, and low-cost analysis of the data. While exploring the landscape of big data analytics, multiple challenges arise from three characteristics of big data: the volume, the variety, and the velocity. High volume and velocity of the data warrant a large amount of storage, memory, and compute power while a large variety of data demands cognition across domains. Addressing domain-intrinsic properties of data can help us analyze the data efficiently through the frugal use of high-performance compu

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Zuo, Liudong. "Efficient Bandwidth Reservation Strategies for Data Movements on High Performance Networks." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/dissertations/1055.

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Many next-generation e-science applications require fast and reliable transfer of large volumes of data, now frequently termed as ``big data", with guaranteed performance, which is typically enabled by the bandwidth reservation service in high-performance networks (HPNs). Users normally specify the properties and requirements of their data transfers in the bandwidth reservation requests (BRRs), and want to make bandwidth reservations on the HPNs to satisfy the requirements of their data transfers. The challenges of the bandwidth reservation arise from the requirements desired by both the users

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22

Green, Oded. "High performance computing for irregular algorithms and applications with an emphasis on big data analytics." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51860.

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Irregular algorithms such as graph algorithms, sorting, and sparse matrix multiplication, present numerous programming challenges, including scalability, load balancing, and efficient memory utilization. In this age of Big Data we face additional challenges since the data is often streaming at a high velocity and we wish to make near real-time decisions for real-world events. For instance, we may wish to track Twitter for the pandemic spread of a virus. Analyzing such data sets requires combing algorithmic optimizations and utilization of massively multithreaded architectures, accelerator such

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Jose, Jithin. "Designing High Performance and Scalable Unified Communication Runtime (UCR) for HPC and Big Data Middleware." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1406202210.

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Islam, Nusrat Sharmin. "High Performance File System and I/O Middleware Design for Big Data on HPC Clusters." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480476699154944.

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Teets, Jay Marshall. "Multidimensional Visualization of Process Monitoring and Quality Assurance Data in High-Volume Discrete Manufacturing." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/26156.

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Advances in microcomputing hardware and software over the last several years have resulted in personal computers with exceptional computational power and speed. As the costs associated with microcomputer hardware and software continue to decline, manufacturers have begun to implement numerous information technology components on the shop floor. Components such as microcomputer file servers and client workstations are replacing traditional (manual) methods of data collection and analysis since they can be used as a tool for real-time decision-making. Server-based and web-based shop floor dat

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Huai, Yin. "Building High Performance Data Analytics Systems based on Scale-out Models." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1427553721.

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Dao, Quang Minh. "High performance processing of metagenomics data." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS203.

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Avec l'avènement de la technologie de séquençage de la prochaine génération, une quantité sans cesse croissante de données génomiques est produite à mesure que le coût du séquençage diminue. Cela a permis au domaine de la métagénomique de se développer rapidement. Par conséquent, la communauté bioinformatique est confrontée à des goulots d'étranglement informatiques sans précédent pour traiter les énormes ensembles de données métagénomiques. Les pipelines traditionnels de métagénomique se composent de plusieurs étapes, utilisant différentes plates-formes de calcul distribuées et parallèles pou

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Soukup, Petr. "High-Performance Analytics (HPA)." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165252.

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The aim of the thesis on the topic of High-Performance Analytics is to gain a structured overview of solutions of high performance methods for data analysis. The thesis introduction concerns with definitions of primary and secondary data analysis, and with the primary systems which are not appropriate for analytical data analysis. The usage of mobile devices, modern information technologies and other factors caused a rapid change of the character of data. The major part of this thesis is devoted particularly to the historical turn in the new approaches towards analytical data analysis, which w

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Wang, Wei. "Unveiling Molecular Mechanisms of piRNA Pathway from Small Signals in Big Data: A Dissertation." eScholarship@UMMS, 2015. https://escholarship.umassmed.edu/gsbs_diss/805.

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PIWI-interacting RNAs (piRNA) are a group of 23–35 nucleotide (nt) short RNAs that protect animal gonads from transposon activities. In Drosophila germ line, piRNAs can be categorized into two different categories— primary and secondary piRNAs— based on their origins. Primary piRNAs, generated from transcripts of specific genomic regions called piRNA clusters, which are enriched in transposon fragments that are unlikely to retain transposition activity. The transcription and maturation of primary piRNAs from those cluster transcripts are poorly understood. After being produced, a group of prim

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Wang, Wei. "Unveiling Molecular Mechanisms of piRNA Pathway from Small Signals in Big Data: A Dissertation." eScholarship@UMMS, 2010. http://escholarship.umassmed.edu/gsbs_diss/805.

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PIWI-interacting RNAs (piRNA) are a group of 23–35 nucleotide (nt) short RNAs that protect animal gonads from transposon activities. In Drosophila germ line, piRNAs can be categorized into two different categories— primary and secondary piRNAs— based on their origins. Primary piRNAs, generated from transcripts of specific genomic regions called piRNA clusters, which are enriched in transposon fragments that are unlikely to retain transposition activity. The transcription and maturation of primary piRNAs from those cluster transcripts are poorly understood. After being produced, a group of prim

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Zheng, Fang. "Middleware for online scientific data analytics at extreme scale." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51847.

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Scientific simulations running on High End Computing machines in domains like Fusion, Astrophysics, and Combustion now routinely generate terabytes of data in a single run, and these data volumes are only expected to increase. Since such massive simulation outputs are key to scientific discovery, the ability to rapidly store, move, analyze, and visualize data is critical to scientists' productivity. Yet there are already serious I/O bottlenecks on current supercomputers, and movement toward the Exascale is further accelerating this trend. This dissertation is concerned with the design, impleme

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Lemon, Alexander Michael. "A Shared-Memory Coupled Architecture to Leverage Big Data Frameworks in Prototyping and In-Situ Analytics for Data Intensive Scientific Workflows." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7545.

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There is a pressing need for creative new data analysis methods whichcan sift through scientific simulation data and produce meaningfulresults. The types of analyses and the amount of data handled by currentmethods are still quite restricted, and new methods could providescientists with a large productivity boost. New methods could be simpleto develop in big data processing systems such as Apache Spark, which isdesigned to process many input files in parallel while treating themlogically as one large dataset. This distributed model, combined withthe large number of analysis libraries created f

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Schintler, Laurie A., and Manfred M. Fischer. "The Analysis of Big Data on Cites and Regions - Some Computational and Statistical Challenges." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6637/1/2018%2D10%2D28_Big_Data_on_cities_and_regions_untrack_changes.pdf.

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Big Data on cities and regions bring new opportunities and challenges to data analysts and city planners. On the one side, they hold great promise to combine increasingly detailed data for each citizen with critical infrastructures to plan, govern and manage cities and regions, improve their sustainability, optimize processes and maximize the provision of public and private services. On the other side, the massive sample size and high-dimensionality of Big Data and their geo-temporal character introduce unique computational and statistical challenges. This chapter provides overviews on the sal

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Mahapatra, Tanmaya [Verfasser], Christian [Akademischer Betreuer] Prehofer, Christian [Gutachter] Prehofer, and Florian [Gutachter] Matthes. "High-level Graphical Programming for Big Data Applications / Tanmaya Mahapatra ; Gutachter: Christian Prehofer, Florian Matthes ; Betreuer: Christian Prehofer." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/120108640X/34.

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Espinosa-Carrasco, José. "Big behavioral data analysis : computational methods for the study of continuous recordings behavior." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/552411.

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New high-throughput behavioral systems enable the recording of continuous behavioral sequences with an unprecedented richness of signals and a deep temporal resolution. Automated systems offer neuroscience the opportunity to tackle in a new way the old question of how the brain orchestrates behavior and ultimately understand brain function itself, however, they accumulate large amounts of data leading to what is being termed Big Behavioral Data. The manipulation, analysis and contextualization of these data to obtain useful biological insights is not a trivial problem. This thesis presents Pe

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Saeed, Ifrah. "A portable relational algebra library for high performance data-intensive query processing." Thesis, Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51967.

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A growing number of industries are turning to data warehousing applications such as forecasting and risk assessment to process large volumes of data. These data warehousing applications, which utilize queries comprised of a mix of arithmetic and relational algebra (RA) operators, currently run on systems that utilize commodity multi-core CPUs. If we acknowledge the data-intensive nature of these applications, general purpose graphics processing units (GPUs) with high throughput and memory bandwidth seem to be natural candidates to host these applications. However, since such relational queries

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Chen, Xiao Verfasser], and Gunter [Gutachter] [Saake. "Towards efficient and effective entity resolution for high-volume and variable data / Xiao Chen ; Gutachter: Gunter Saake." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2020. http://d-nb.info/122361557X/34.

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Chen, Xiao [Verfasser], and Gunter [Gutachter] Saake. "Towards efficient and effective entity resolution for high-volume and variable data / Xiao Chen ; Gutachter: Gunter Saake." Magdeburg : Universitätsbibliothek Otto-von-Guericke-Universität, 2020. http://d-nb.info/122361557X/34.

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Caie, Peter David. "Discovery of novel prognostic tools to stratify high risk stage II colorectal cancer patients utilising digital pathology." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/19527.

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Colorectal cancer (CRC) patients are stratified by the Tumour, Node and Metastasis (TNM) staging system for clinical decision making. Additional genomic markers have a limited utility in some cases where precise targeted therapy may be available. Thus, classical clinical pathological staging remains the mainstay of the assessment of this disease. Surgical resection is generally considered curative for Stage II patients, however 20-30% of these patients experience disease recurrence and disease specific death. It is imperative to identify these high risk patients in order to assess if further t

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Honore, Valentin. "Convergence HPC - Big Data : Gestion de différentes catégories d'applications sur des infrastructures HPC." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0145.

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Le calcul haute performance est un domaine scientifique dans lequel de très complexes et intensifs calculs sont réalisés sur des infrastructures de calcul à très large échelle appelées supercalculateurs. Leur puissance calculatoire phénoménale permet aux supercalculateurs de générer un flot de données gigantesque qu'il est aujourd'hui difficile d'appréhender, que ce soit d'un point de vue du stockage en mémoire que de l'extraction des résultats les plus importants pour les applications.Nous assistons depuis quelques années à une convergence entre le calcul haute performance et des domaines tel

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Sharma, Rahil. "Shared and distributed memory parallel algorithms to solve big data problems in biological, social network and spatial domain applications." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2277.

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Big data refers to information which cannot be processed and analyzed using traditional approaches and tools, due to 4 V's - sheer Volume, Velocity at which data is received and processed, and data Variety and Veracity. Today massive volumes of data originate in domains such as geospatial analysis, biological and social networks, etc. Hence, scalable algorithms for effcient processing of this massive data is a signicant challenge in the field of computer science. One way to achieve such effcient and scalable algorithms is by using shared & distributed memory parallel programming models. In thi

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Eilertsen, Gabriel. "High-resolution simulation and rendering of gaseous phenomena from low-resolution data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70269.

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Numerical simulations are often used in computer graphics to capture the effects of natural phenomena such as fire, water and smoke. However, simulating large-scale events in this way, with the details needed for feature film, poses serious problems. Grid-based simulations at resolutions sufficient to incorporate small-scale details would be costly and use large amounts of memory, and likewise for particle based techniques. To overcome these problems, a new framework for simulation and rendering of gaseous phenomena is presented in this thesis. It makes use of a combination of different existi

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Korndorfer, Jonas Henrique Muller. "High performance trace replay event simulation of parallel programs behavior." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/149310.

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Sistemas modernos de alto desempenho compreendem milhares a milhões de unidades de processamento. O desenvolvimento de uma aplicação paralela escalável para tais sistemas depende de um mapeamento preciso da utilização recursos disponíveis. A identificação de recursos não utilizados e os gargalos de processamento requere uma boa análise desempenho. A observação de rastros de execução é uma das técnicas mais úteis para esse fim. Infelizmente, o rastreamento muitas vezes produz grandes arquivos de rastro, atingindo facilmente gigabytes de dados brutos. Portanto ferramentas para análise de desempe

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Cyrus, Sam. "Fast Computation on Processing Data Warehousing Queries on GPU Devices." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6214.

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Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to process each individual query, which results in underutilization of GPU. When a single query data warehousing workload was run on an open source GPU query engine, the utilization of main GPU resources was found to be less than 25%. The low utilization then leads to low system throughput. To resolve this problem, this paper suggests a way to transfer all of the desired data into the global memory of GPU and keep it until all queries are executed as one batch. The PCIe transfer time from CPU to G

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Nilsson, Mårten. "Augmenting High-Dimensional Data with Deep Generative Models." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233969.

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Data augmentation is a technique that can be performed in various ways to improve the training of discriminative models. The recent developments in deep generative models offer new ways of augmenting existing data sets. In this thesis, a framework for augmenting annotated data sets with deep generative models is proposed together with a method for quantitatively evaluating the quality of the generated data sets. Using this framework, two data sets for pupil localization was generated with different generative models, including both well-established models and a novel model proposed for this pu

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LAUDATO, Gennaro. "Innovative information systems to monitor biomedical parameters during high demanding tasks." Doctoral thesis, Università degli studi del Molise, 2021. http://hdl.handle.net/11695/100496.

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The objective of this PhD project is, as its research core, the application of Machine Learning techniques and Big Data analytics to monitor, in a non-invasive way, vital parameters of individuals engaged in tasks that require a high psychophysical effort. The industrial partners of this project are Formula Medicine (as Italian industrial partner with advisor Dr. Riccardo Ceccarelli) and AOTech (foreign industrial partner with advisor mr. Sebastien Philippe). Formula Medicine is a sports medicine center able to offer medical assistance and training programs both physical and mental. Its str

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Saxena, Rishu. "Towards a Polyalgorithm for Land Use and Land Cover Change Detection." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/93177.

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Earth observation satellites (EOS) such as Landsat provide image datasets that can be immensely useful in numerous application domains. One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Several algorithms for time series analysis have been proposed by various groups in remote sensing; more algorithms (that can be adapted) are available in the general time series literature. However, in spite of an abundance of algorithms, the choice of algorithm to be used for analyzing an image stack is presently an open question. A concurrent is

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Hoinka, Jan [Verfasser], and Rolf [Akademischer Betreuer] Backofen. "Aptamers in the age of big data : development and application of algorithmic solutions in the field of high-throughput systematic evolution of ligands by exponental enrichment." Freiburg : Universität, 2016. http://d-nb.info/1122647743/34.

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Chandramowlishwaran, Aparna. "The fast multipole method at exascale." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50388.

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This thesis presents a top to bottom analysis on designing and implementing fast algorithms for current and future systems. We present new analysis, algorithmic techniques, and implementations of the Fast Multipole Method (FMM) for solving N- body problems. We target the FMM because it is broadly applicable to a variety of scientific particle simulations used to study electromagnetic, fluid, and gravitational phenomena, among others. Importantly, the FMM has asymptotically optimal time complexity with guaranteed approximation accuracy. As such, it is among the most attractive solutions for sca

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Vyapamakula, Sreeramachandra Sankeerth. "Expedient Modal Decomposition of Massive Datasets Using High Performance Computing Clusters." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu151515633114873.

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