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Relevant bibliographies by topics / Data Analysis and Cloud Computing

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Dissertations / Theses

Academic literature on the topic 'Data Analysis and Cloud Computing'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Dissertations / Theses on the topic "Data Analysis and Cloud Computing"

1

Owen, Anne M. "Widescale analysis of transcriptomics data using cloud computing methods." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16125/.

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This study explores the handling and analyzing of big data in the field of bioinformatics. The focus has been on improving the analysis of public domain data for Affymetrix GeneChips which are a widely used technology for measuring gene expression. Methods to determine the bias in gene expression due to G-stacks associated with runs of guanine in probes have been explored via the use of a grid and various types of cloud computing. An attempt has been made to find the best way of storing and analyzing big data used in bioinformatics. A grid and various types of cloud computing have been employe

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2

Jayapandian, Catherine Praveena. "Cloudwave: A Cloud Computing Framework for Multimodal Electrophysiological Big Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405516626.

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3

Flatt, Taylor. "CrowdCloud: Combining Crowdsourcing with Cloud Computing for SLO Driven Big Data Analysis." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2234.

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The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and

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4

Shand, Corinne Margaret. "Investigating data standardisation and modelling challenges to enable advanced power systems analysis." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/16362.

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As the power industry moves towards more active distribution networks there is an increased requirement for greater analysis and observability of the current state of the network. There are a number of challenges for utilities in realising this including the quality and accuracy of their network models; the lack of integration between network models and the large quantities of sensor data being collected; the security and communication challenges posed when installing large numbers of sophisticated sensors across distribution networks; and the exponential increase in computing power required t

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5

Pereira, Rosangela de Fátima. "A data-driven solution for root cause analysis in cloud computing environments." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-03032017-082237/.

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The failure analysis and resolution in cloud-computing environments are a a highly important issue, being their primary motivation the mitigation of the impact of such failures on applications hosted in these environments. Although there are advances in the case of immediate detection of failures, there is a lack of research in root cause analysis of failures in cloud computing. In this process, failures are tracked to analyze their causal factor. This practice allows cloud operators to act on a more effective process in preventing failures, resulting in the number of recurring failures reduct

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6

Bandini, Alessandro. "Programming and Deployment of Cloud-based Data Analysis Applications." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13803/.

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Cloud Computing constitutes a model capable of enabling the network access in a shared, practical and on demand of different computational resources like networks, memory, application or services. This work has as goal the explanation of the project made within Cloud Computing. After an introduction of the theory that lies behind Cloud computing's technologies, there is the practical part of the the work, starting from a more specific platform, Hadoop, which allows storage and data analysis and then moving to more general purpose platforms, Amazon Web Services and Google App Engine, where di

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7

Saker, Vanessa. "Automated feature synthesis on big data using cloud computing resources." Master's thesis, University of Cape Town, 2020. http://hdl.handle.net/11427/32452.

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The data analytics process has many time-consuming steps. Combining data that sits in a relational database warehouse into a single relation while aggregating important information in a meaningful way and preserving relationships across relations, is complex and time-consuming. This step is exceptionally important as many machine learning algorithms require a single file format as an input (e.g. supervised and unsupervised learning, feature representation and feature learning, etc.). An analyst is required to manually combine relations while generating new, more impactful information points fr

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8

Ribot, Stephane. "Adoption of Big Data And Cloud Computing Technologies for Large Scale Mobile Traffic Analysis." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE3049.

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L’émergence des technologies Big Data et Cloud computing pour répondre à l’accroissement constant de la complexité et de la diversité des données constituent un nouvel enjeu de taille pour les entreprises qui, désormais, doivent prendre en compte ce nouveau paradigme. Les opérateurs de services mobiles sont un exemple de sociétés qui cherchent à valoriser et monétiser les données collectées de leur utilisateurs. Cette recherche a pour objectif d’analyser ce nouvel enjeu qui allie d’une part l’explosion du nombre des données à analyser, et d’autre part, la constante émergence de nouvelles techn

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9

Romanazzi, Stefano. "Water Supply Network Management: Sensor Analysis using Google Cloud Dataflow." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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The growing field of IoT increases the amount of time series data produced every day. With such information overload it is necessary to promptly clean and process those information extracting meaningful knowledge and avoiding raw data storage. Nowadays cloud infrastructures allow to adopt this processing demand by providing new models for defining data-parallel processing pipelines, such as the Apache Beam unified model which evolved from Google Cloud Dataflow and MapReduce paradigm. The projects of this thesis have been implemented during a three-month internship at Injenia srl, and face th

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10

El, Sibai Rayane. "Sampling, qualification and analysis of data streams." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS170/document.

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Un système de surveillance environnementale collecte et analyse continuellement les flux de données générés par les capteurs environnementaux. L'objectif du processus de surveillance est de filtrer les informations utiles et fiables et d'inférer de nouvelles connaissances qui aident l'exploitant à prendre rapidement les bonnes décisions. L'ensemble de ce processus, de la collecte à l'analyse des données, soulève deux problèmes majeurs : le volume de données et la qualité des données. D'une part, le débit des flux de données générés n'a pas cessé d'augmenter sur les dernières années, engendrant

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