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Dissertations / Theses on the topic 'Big data in healthcare'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Castaño, Martínez María, and Elizabeth Johnson. "Communicating big data in the healthcare industry." Thesis, Linnéuniversitetet, Institutionen för marknadsföring (MF), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-96251.

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In recent years nearly every aspect of how we function as a society has transformed from analogue to digital. This has spurred extraordinary change and acted as a catalyst for technology innovation, as well as big data generation. Big data is characterized by its constantly growing volume, wide variety, high velocity, and powerful veracity. With the emergence of COVID-19, the global pandemic has demonstrated the profound impact, and often dangerous consequences, when communicating health information derived from data. Healthcare companies have access to enormous data assets, yet communicating

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Mohamed, M. (Mahmoud). "Platforms for big data business models in the healthcare context." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201906052419.

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Abstract. The profitability of the business opportunity is defined by the level of owned data and its insights to the business organization. However, the existing literature has not identified how to link between different business models in the data-oriented systems. The previous research efforts focused on the technical aspects of data including data monetization, clustering, and data lifecycle. The purpose of this research is to understand how to link big data and business model thinking in the healthcare context. The main argument of this study provides a novel way to the modularity in the

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Mgudlwa, Sibulela. "A big data analytics framework to improve healthcare service delivery in South Africa." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2877.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2018.<br>Healthcare facilities in South Africa accumulate big data, daily. However, this data is not being utilised to its full potential. The healthcare sector still uses traditional methods to store, process, and analyse data. Currently, there are no big data analytics tools being used in the South African healthcare environment. This study was conducted to establish what factors hinder the effective use of big data in the South African healthcare environment. To fulfil the objectives of this research, qualita

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Nováková, Martina. "Analýza Big Data v oblasti zdravotnictví." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-201737.

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This thesis deals with the analysis of Big Data in healthcare. The aim is to define the term Big Data, to acquaint the reader with data growth in the world and in the health sector. Another objective is to explain the concept of a data expert and to define team members of the data experts team. In following chapters phases of the Big Data analysis according to methodology of EMC2 company are defined and basic technologies for analysing Big Data are described. As beneficial and interesting I consider the part dealing with definition of tasks in which Big Data technologies are already used in he

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Pšurný, Michal. "Big data analýzy a statistické zpracování metadat v archivu obrazové zdravotnické dokumentace." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316821.

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This Diploma thesis describes issues of big data in healthcare focus on picture archiving and communication system. DICOM format are store images with header where it could be other valuable information. This thesis mapping data from 1215 studies.

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Jonsson, Hanna, and Luyolo Mazomba. "Revenue Generation in Data-driven Healthcare : An exploratory study of how big data solutions can be integrated into the Swedish healthcare system." Thesis, Umeå universitet, Företagsekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-161384.

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Abstract The purpose of this study is to investigate how big data solutions in the Swedish healthcare system can generate a revenue. As technology continues to evolve, the use of big data is beginning to transform processes in many different industries, making them more efficient and effective. The opportunities presented by big data have been researched to a large extent in commercial fields, however, research in the use of big data in healthcare is scarce and this is particularly true in the case of Sweden. Furthermore, there is a lack in research that explores the interface between big data

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Jamthe, Anagha. "Mitigating interference in Wireless Body Area Networks and harnessing big data for healthcare." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1445341798.

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Saenyi, Betty. "Opportunities and challenges of Big Data Analytics in healthcare : An exploratory study on the adoption of big data analytics in the Management of Sickle Cell Anaemia." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-42864.

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Background: With increasing technological advancements, healthcare providers are adopting electronic health records (EHRs) and new health information technology systems. Consequently, data from these systems is accumulating at a faster rate creating a need for more robust ways of capturing, storing and processing the data. Big data analytics is used in extracting insight form such large amounts of medical data and is increasingly becoming a valuable practice for healthcare organisations. Could these strategies be applied in disease management? Especially in rare conditions like Sickle Cell Dis

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Wang, Mengyuan. "The way of chinese medical reform : new trends in the era of the “internet+” and big data." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/18585.

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Mestrado em Desenvolvimento e Cooperação Internacional<br>A China é um país com uma população imensa, com recursos médicos insuficientes e distribuição desigual. Portanto, existem muitos problemas no serviço de saúde. Devido ao desenvolvimento atrasado do sistema médico, a qualidade dos recursos médicos é baixa, o custo é alto e a eficiência dos serviços médicos é baixa. Um dos principais fatores explicativos dessa situação é a falta de apoio do governo e seguro médico imperfeito. Para resolver esse problema, o governo começou a reformar o sistema de segurança médica. Desde a reforma do seguro

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Ramadoss, Balaji. "Ontology Driven Model for an Engineered Agile Healthcare System." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5110.

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Healthcare is in urgent need of an effective way to manage the complexity it of its systems and to prepare quickly for immense changes in the economics of healthcare delivery and reimbursement. Centers for Medicare & Medicaid Services (CMS) releases policies affecting inpatient and long-term care hospitals policies that directly affect reimbursement and payment rates. One of these policy changes, a quality-reporting program called Hospital Inpatient Quality Reporting (IQR), will effect approximately 3,400 acute-care and 440 long-term care hospitals. IQR sets guidelines and measures that will c

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Corné, Josefine, and Amanda Ullvin. "Prediktiv analys i vården : Hur kan maskininlärningstekniker användas för att prognostisera vårdflöden?" Thesis, KTH, Skolan för teknik och hälsa (STH), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-211286.

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Projektet genomfördes i samarbete med Siemens Healthineers i syfte att utreda möjligheter till att prognostisera vårdflöden. Det genom att undersöka hur big data tillsammans med maskininlärning kan utnyttjas för prediktiv analys. Projektet utgjordes av två fallstudier med mål att, baserat på data från tidigare MRT-undersökningar, förutspå undersökningstider för kommande undersökningar respektive identifiera patienter som riskerar att missa inbokad undersökning. Fallstudierna utfördes med hjälp av programmeringsspråket R och tre olika inbyggda funktioner för maskininlärning användes för att ta

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Jaffré, Marc-Olivier. "Connaissance et optimisation de la prise en charge des patients : la science des réseaux appliquée aux parcours de soins." Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2445/document.

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En France, la nécessaire rationalisation des moyens alloués aux hôpitaux a abouti à une concentration des ressources et une augmentation de la complexité des plateaux techniques. Leur pilotage et leur répartition territoriale s’avèrent d’autant plus difficile, soulevant ainsi la problématique de l’optimisation des systèmes de soins. L’utilisation des données massives produites pas ces systèmes pourrait constituer une nouvelle approche en matière d’analyse et d’aide à la décision. Méthode : A partir d’une réflexion sur la notion de performance, différentes approches d’optimisation préexistantes

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Reda, Roberto. "A Semantic Web approach to ontology-based system: integrating, sharing and analysing IoT health and fitness data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14645/.

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With the rapid development of fitness industry, Internet of Things (IoT) technology is becoming one of the most popular trends for the health and fitness areas. IoT technologies have revolutionised the fitness and the sport industry by giving users the ability to monitor their health status and keep track of their training sessions. More and more sophisticated wearable devices, fitness trackers, smart watches and health mobile applications will appear in the near future. These systems do collect data non-stop from sensors and upload them to the Cloud. However, from a data-centric perspective

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Hansen, Simon, and Erik Markow. "Big Data : Implementation av Big Data i offentlig verksamhet." Thesis, Högskolan i Halmstad, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38756.

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Lundvall, Helena. "Big data = Big money? : En kvantitativ studie om big data, förtroende och köp online." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451065.

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Tidigare forskning har entydigt visat på att ett ökat förtroende hos kunder i köpsituationer ökar deras vilja att genomföra köp. Vilka faktorer som påverkar kunders förtroende har även det undersökts flitigt och faktorer som kan kopplas till hantering av kunders data tas allt oftare upp som avgörande. Dock behandlas dessa faktorer många gånger på ett övergripande plan och studier som djupdyker i vilka underliggande faktorer kopplat till datahantering som påverkar kunders förtroende saknas. Genom att samla in kvantitativ data om hur kunder förhåller sig till företags insamling och användande av

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Rizk, Raya. "Big Data Validation." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353850.

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With the explosion in usage of big data, stakes are high for companies to develop workflows that translate the data into business value. Those data transformations are continuously updated and refined in order to meet the evolving business needs, and it is imperative to ensure that a new version of a workflow still produces the correct output. This study focuses on the validation of big data in a real-world scenario, and implements a validation tool that compares two databases that hold the results produced by different versions of a workflow in order to detect and prevent potential unwanted a

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Jaber, Carolin. "Big data visualisering." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-79898.

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Visualisering av data i grafiska presentationer är viktigt inom många olika områden för attenklare förstå information och relationer av insamlad data. Mängden data växer snabbt tillstora skalor som är svåra att hantera och bidrar till nya utmaningar vid visualisering av data igrafiska presentationer. System är beroende av data visualisering för att upptäcka defekteroch fel av produktion. Genom att förbättra prestandan av tidsseriedata visualisering ökar detmöjligheten att upptäcka fel och defekter av produktion.Rapporten tar upp metoder för visualisering av tidsseriedata med snabb prestanda oc

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Blahová, Leontýna. "Big Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203994.

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This master thesis is about Big Data Governance and about software, which is used for this purposes. Because Big Data are huge opportunity and also risk, I wanted to map products which can be easily use for Data Quality and Big Data Governance in one platform. This thesis is not only on theoretical knowledge level, but also evaluates five key products (from my point of view). I defined requirements for every kind of domain and then I set up the weights and points. The main objective is to evaluate software capabilities and compere them.

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Casagrande, Federico <1994&gt. "Big Data Valuation." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19687.

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Kämpe, Gabriella. "How Big Data Affects UserExperienceReducing cognitive load in big data applications." Thesis, Umeå universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163995.

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We have entered the age of big data. Massive data sets are common in enterprises, government, and academia. Interpreting such scales of data is still hard for the human mind. This thesis investigates how proper design can decrease the cognitive load in data-heavy applications. It focuses on numeric data describing economic growth in retail organizations. It aims to answer the questions: What is important to keep in mind when designing an interface that holds large amounts of data? and How to decrease the cognitive load in complex user interfaces without reducing functionality?. It aims to answ

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Sherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.

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This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is anal

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Giordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.

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Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrast

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Francke, Angela, and Sven Lißner. "Big Data im Radverkehr." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230730.

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Für einen attraktiven Radverkehr bedarf es einer qualitativ hochwertigen Infrastruktur. Bisher liegen durch den hohen Aufwand von Vor-Ort-Erfassungen nur punktuelle Radverkehrsstärken vor. Die aktuell wohl zuverlässigsten und tauglichsten Werte liefern bisher fest installierte automatische Radverkehrszählstellen, wie sie bereits viele Kommunen installiert haben. Ein Nachteil ist hierbei, dass für eine flächige Abdeckung mit einer besseren Aussagekraft für die gesamte Stadt oder Kommune die Anzahl der Erhebungspunkte meist deutlich zu gering ist. Die Bedeutung des Nebennetzes für den Radverkehr

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Santos, Lúcio Fernandes Dutra. "Similaridade em big data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07022018-104929/.

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Os volumes de dados armazenados em grandes bases de dados aumentam em ritmo sempre crescente, pressionando o desempenho e a flexibilidade dos Sistemas de Gerenciamento de Bases de Dados (SGBDs). Os problemas de se tratar dados em grandes quantidades, escopo, complexidade e distribuição vêm sendo tratados também sob o tema de big data. O aumento da complexidade cria a necessidade de novas formas de busca - representar apenas números e pequenas cadeias de caracteres já não é mais suficiente. Buscas por similaridade vêm se mostrando a maneira por excelência de comparar dados complexos, mas até re

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Francke, Angela, and Sven Lißner. "Big Data im Radverkehr." Technische Universität Dresden, 2017. https://tud.qucosa.de/id/qucosa%3A29637.

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Für einen attraktiven Radverkehr bedarf es einer qualitativ hochwertigen Infrastruktur. Bisher liegen durch den hohen Aufwand von Vor-Ort-Erfassungen nur punktuelle Radverkehrsstärken vor. Die aktuell wohl zuverlässigsten und tauglichsten Werte liefern bisher fest installierte automatische Radverkehrszählstellen, wie sie bereits viele Kommunen installiert haben. Ein Nachteil ist hierbei, dass für eine flächige Abdeckung mit einer besseren Aussagekraft für die gesamte Stadt oder Kommune die Anzahl der Erhebungspunkte meist deutlich zu gering ist. Die Bedeutung des Nebennetzes für den Radverkehr

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Виноградова, О. В. "Використання Big Data компаніями". Thesis, Київський національний універститет технологій та дизайну, 2017. https://er.knutd.edu.ua/handle/123456789/10417.

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Blaho, Matúš. "Aplikace pro Big Data." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385977.

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This work deals with the description and analysis of the Big Data concept and its processing and use in the process of decision support. Suggested processing is based on the MapReduce concept designed for Big Data processing. The theoretical part of this work is largely about the Hadoop system that implements this concept. Its understanding is a key feature for properly designing applications that run within it. The work also contains design for specific Big Data processing applications. In the implementation part of the thesis is a description of Hadoop system management, description of imple

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Flike, Felix, and Markus Gervard. "BIG DATA-ANALYS INOM FOTBOLLSORGANISATIONER En studie om big data-analys och värdeskapande." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20117.

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Big data är ett relativt nytt begrepp men fenomenet har funnits länge. Det går att beskriva utifrån fem V:n; volume, veracity, variety, velocity och value. Analysen av Big Data har kommit att visa sig värdefull för organisationer i arbetet med beslutsfattande, generering av mätbara ekonomiska fördelar och förbättra verksamheten. Inom idrottsbranschen började detta på allvar användas i början av 2000-talet i baseballorganisationen Oakland Athletics. Man började värva spelare baserat på deras statistik istället för hur bra scouterna bedömde deras förmåga vilket gav stora framgångar. Detta ledde

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Sánchez, Adam. "Big Data, Linked Data y Web semántica." Universidad Peruana de Ciencias Aplicadas (UPC), 2016. http://hdl.handle.net/10757/620705.

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Conferencia realizada en el marco de la Semana del Acceso Abierto Perú, llevada a cabo del 24 al 26 de Octubre de 2016 en Lima, Peru. Las instituciones organizadoras: Universidad Peruana de Ciencias aplciadasd (UPC), Pontificia Universidad Católica del Perú (PUCP) y Universidad Peruana Cayetano Heredia (UPCH).<br>Conferencia que aborda aspectos del protocolo Linked Data, temas de Big Data y Web Semantica,

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Nyström, Simon, and Joakim Lönnegren. "Processing data sources with big data frameworks." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188204.

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Big data is a concept that is expanding rapidly. As more and more data is generatedand garnered, there is an increasing need for efficient solutions that can be utilized to process all this data in attempts to gain value from it. The purpose of this thesis is to find an efficient way to quickly process a large number of relatively small files. More specifically, the purpose is to test two frameworks that can be used for processing big data. The frameworks that are tested against each other are Apache NiFi and Apache Storm. A method is devised in order to, firstly, construct a data flow and sec

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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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Cao, Yang. "Querying big data with bounded data access." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/25421.

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Query answering over big data is cost-prohibitive. A linear scan of a dataset D may take days with a solid state device if D is of PB size and years if D is of EB size. In other words, polynomial-time (PTIME) algorithms for query evaluation are already not feasible on big data. To tackle this, we propose querying big data with bounded data access, such that the cost of query evaluation is independent of the scale of D. First of all, we propose a class of boundedly evaluable queries. A query Q is boundedly evaluable under a set A of access constraints if for any dataset D that satisfies constra

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Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

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Thesis (M.S.C.S.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The rapid development of information technology in recent decades means that data appear in a wide variety of formats — sensor data, tweets, photographs, raw data, and unstructured data. Statistics show that there were 800,000

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KAVOOSIFAR, MOHAMMAD REZA. "Data Mining and Indexing Big Multimedia Data." Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2742526.

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Erlandsson, Niklas. "Game Analytics och Big Data." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-29185.

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Game Analytics är ett område som vuxit fram under senare år. Spelutvecklare har möjligheten att analysera hur deras kunder använder deras produkter ned till minsta knapptryckning. Detta kan resultera i stora mängder data och utmaning ligger i att lyckas göra något vettigt av sitt data. Utmaningarna med speldata beskrivs ofta med liknande egenskaper som används för att beskriva Big Data: volume, velocity och variability. Detta borde betyda att det finns potential för ett givande samarbete. Studiens syfte är att analysera och utvärdera vilka möjligheter Big Data ger att utveckla området Game Ana

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Francke, Angela, and Sven Lißner. "Big Data in Bicycle Traffic." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-233278.

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For cycling to be attractive, the infrastructure must be of high quality. Due to the high level of resources required to record it locally, the available data on the volume of cycling traffic has to date been patchy. At the moment, the most reliable and usable numbers seem to be derived from permanently installed automatic cycling traffic counters, already used by many local authorities. One disadvantage of these is that the number of data collection points is generally far too low to cover the entirety of a city or other municipality in a way that achieves truly meaningful results. The effect

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Doucet, Rachel A., Deyan M. Dontchev, Javon S. Burden, and Thomas L. Skoff. "Big data analytics test bed." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37615.

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Approved for public release; distribution is unlimited<br>The proliferation of big data has significantly expanded the quantity and breadth of information throughout the DoD. The task of processing and analyzing this data has become difficult, if not infeasible, using traditional relational databases. The Navy has a growing priority for information processing, exploitation, and dissemination, which makes use of the vast network of sensors that produce a large amount of big data. This capstone report explores the feasibility of a scalable Tactical Cloud architecture that will harness and utiliz

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Lansley, Guy David. "Big data : geodemographics and representation." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10045119/.

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Due to the harmonisation of data collection procedures with everyday activities, Big Data can be harnessed to produce geodemographic representations to supplement or even replace traditional sources of population data which suffer from low response rates or intermittent refreshes. Furthermore, the velocity and diversity of new forms of data also enable the creation entirely new forms of geodemographic insight. However, their miscellaneous data collection procedures are inconsistent, unregulated and are not robustly sampled like conventional social sciences data sources. Therefore, uncertainty

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Cao, Lei. "Outlier Detection In Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/82.

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The dissertation focuses on scaling outlier detection to work both on huge static as well as on dynamic streaming datasets. Outliers are patterns in the data that do not conform to the expected behavior. Outlier detection techniques are broadly applied in applications ranging from credit fraud prevention, network intrusion detection to stock investment tactical planning. For such mission critical applications, a timely response often is of paramount importance. Yet the processing of outlier detection requests is of high algorithmic complexity and resource consuming. In this dissertation we inv

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Talbot, David. "Bloom maps for big data." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/25235.

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The ability to retrieve a value given a key is fundamental in computer science. Unfortunately as the a priori set from which keys are drawn grows in size, any exact data structure must use more space per key. This motivates our interest in approximate data structures. We consider the problem of succinctly encoding a map to support queries with bounded error when the distribution over values is known. We give a lower bound on the space required per key in terms of the entropy of the distribution over values and the error rate and present a generalization of the Bloom filter, the Bloom map, that

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Rupprecht, Lukas. "Network-aware big data processing." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/52455.

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The scale-out approach of modern data-parallel frameworks such as Apache Flink or Apache Spark has enabled them to deal with large amounts of data. These applications are often deployed in large-scale data centres with many resources. However, as deployments and data continue to grow, more network communication is incurred during a data processing query. At the same time, data centre networks (DCNs) are becoming increasingly more complex in terms of the physical network topology, the variety of applications that are sharing the network, and the different requirements of these applications on t

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Andersson, Andreas. "Big data - det nya hälsoverktyget?" Thesis, Linnéuniversitetet, Institutionen för idrottsvetenskap (ID), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-56519.

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En inblick i ett nytt och snabbt växande område. En studie som undersöker användningen av datainsamling och Big data inom hälsoföretag. Syftet grundas i att skapa en kunskap och medvetenhet om hur det i dagens hälsoföretag ser ut inom denna del. Genom en granskning av nio företags användarvillkor samt deras integritetspolicy finner vi att samtliga företag samlar och spar data om sina kunder. Insamlingen sker utan användarens vetskap och denna Big data delas sedan vidare till andra företag som har användning för den.

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Слишинська, В. О., та Ігор Віталійович Пономаренко. "Використання Big Data в маркетингу". Thesis, КНУТД, 2016. https://er.knutd.edu.ua/handle/123456789/4082.

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Панферова, И. Ю. "Анализ неструктурированных данных big data". Thesis, Академія внутрішніх військ МВС України, 2017. http://openarchive.nure.ua/handle/document/9973.

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Luo, Changqing. "Towards Secure Big Data Computing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1529929603348119.

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Šoltýs, Matej. "Big Data v technológiách IBM." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193914.

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This diploma thesis presents Big Data technologies and their possible use cases and applications. Theoretical part is initially focused on definition of term Big Data and afterwards is focused on Big Data technology, particularly on Hadoop framework. There are described principles of Hadoop, such as distributed storage and data processing, and its individual components. Furthermore are presented the largest vendors of Big Data technologies. At the end of this part of the thesis are described possible use cases of Big Data technologies and also some case studies. The practical part describes im

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Miloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.

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The thesis covers the term for specific data analysis called Big Data. The thesis firstly defines the term Big Data and the need for its creation because of the rising need for deeper data processing and analysis tools and methods. The thesis also covers some of the technical aspects of Big Data tools, focusing on Apache Hadoop in detail. The later chapters contain Big Data market analysis and describe the biggest Big Data competitors and tools. The practical part of the thesis presents a way of using Apache Hadoop to perform data analysis with data from Twitter and the results are then visual

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Potter, Justin Gregory. "Big data adoption in SMMEs." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52297.

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Big data and the use of big data analytics is being adopted more frequently, especially in large organisations that have the resources to deploy it. Big data analytics is allowing businesses to optimise operations and gain deeper insights into their customers needs and behaviours. There is, however, almost no published research into how big data analytics is being used by SMMEs and how they are doing this despite having constrained resources. The objective of this research was to explore the factors that contribute to the adoption of big data analytics in SMMEs. Nine qualitative, semi-struct

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Al-Salim, Ali Mahdi Ali. "Energy efficient big data networks." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/20640/.

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The continuous increase of big data applications in number and types creates new challenges that should be tackled by the green ICT community. Data scientists classify big data into four main categories (4Vs): Volume (with direct implications on power needs), Velocity (with impact on delay requirements), Variety (with varying CPU requirements and reduction ratios after processing) and Veracity (with cleansing and backup constraints). Each V poses many challenges that confront the energy efficiency of the underlying networks carrying big data traffic. In this work, we investigated the impact of

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Neagu, Daniel, and A.-N. Richarz. "Big data in predictive toxicology." Royal Society of Chemistry, 2019. http://hdl.handle.net/10454/17603.

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No<br>The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the

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