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Дисертації з теми "Big data in logistics"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 липня 2025

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1

Zubar, Tymofiy, Тимофій Андрійович Зубар, Olena Volovyk, and Олена Іванівна Воловик. "Big data in logistics: last mile application." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50494.

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1. 3PL Survey - https://www.3plstudy.com/ 2. 5 Examples of How Big Data in Logistics Can Transform The Supply Chain - https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain<br>Big data is revolutionizing many business areas, including logistics and business processes in it. The complexity and dynamics of logistics, coupled with the reliance on many movable parts, can cause bottlenecks at any point in the supply chain, making big data application a vital element of effectiveness in logistical processes design and management. For example, big data logistics can be used to o

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2

Baitalmal, Mohammad Hamza. "A Grounded Theory Model of the Relationship between Big Data and an Analytics Driven Supply Chain Competitive Strategy." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404511/.

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Анотація:

The technology for storing and using big data is evolving rapidly and those that can keep pace are likely to garner additional competitive advantages. One approach to uncovering existing practice in a manner that provides insights for building theory is the use of grounded theory. The current research employs qualitative research following a grounded theory approach to explore gap in understanding the relationship between big data (BD) and the supply chain (SC). In this study eight constructs emerged: Organizational and environmental factors, big data and supply chain analytics, alignment, dat

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3

ADAMASHVILI, NINO. "Big data analytics tools for improving the decision-making process in agrifood supply chain." Doctoral thesis, Università di Foggia, 2021. https://hdl.handle.net/11369/425167.

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Introduzione: Nell'interesse di garantire una sicurezza alimentare a lungo termine di fronte a circostanze mutevoli, è necessario comprendere e considerare gli aspetti ambientali, sociali ed economici del processo di produzione. Inoltre, a causa della globalizzazione, sono stati sollevati i problemi delle lunghe filiere agroalimentari, l'asimmetria informativa, la contraffazione, la difficoltà di tracciare e rintracciare l'origine dei prodotti e le numerose questioni correlate quali il benessere dei consumatori e i costi sanitari. Le tecnologie emergenti guidano verso il raggiungimento di nuov

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4

Baitalmal, Mohammad Hamza. "A Grounded Theory Model of the Relationship between Big Data and an Analytics-Driven Supply Chain Competitive Strategy." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc1404511/.

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Анотація:

The technology for storing and using big data is evolving rapidly and those that can keep pace are likely to garner additional competitive advantages. One approach to uncovering existing practice in a manner that provides insights for building theory is the use of grounded theory. The current research employs qualitative research following a grounded theory approach to explore gap in understanding the relationship between big data (BD) and the supply chain (SC). In this study eight constructs emerged: Organizational and environmental factors, big data and supply chain analytics, alignment, dat

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5

Fu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.

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Анотація:

The analysis of big data is of great interest today, and this comes with challenges of improving precision and efficiency in estimation and prediction. We study binary data with covariates from numerous small areas, where direct estimation is not reliable, and there is a need to borrow strength from the ensemble. This is generally done using Bayesian logistic regression, but because there are numerous small areas, the exact computation for the logistic regression model becomes challenging. Therefore, we develop an integrated multivariate normal approximation (IMNA) method for binary data with

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6

陳煜民 and Yuk-man Brian Chan. "Strategy for information management in re-engineering the logistics business." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B3126637X.

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7

Chen, Zhilin. "Bayesian Analysis of Binary Sales Data for Several Industries." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-theses/596.

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Анотація:

The analysis of big data is now very popular. Big data may be very important for companies, societies or even human beings if we can take full advantage of them. Data scientists defined big data with four Vs: volume, velocity, variety and veracity. In a short, the data have large volume, grow with high velocity, represent with numerous varieties and must have high quality. Here we analyze data from many sources (varieties). In small area estimation, the term ``big data' refers to numerous areas. We want to analyze binary for a large number of small areas. Then standard Markov Chain Monte Carlo

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8

Fang, Yuan, and 方媛. "A cost-based model for optimising the construction logisticsschedules." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46080351.

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9

Stiebe, Michael. "#sustainabletransport : A FAIR Cross-Platform Social Media Analysis Approach to Sociotechnical Sustainable Transport Research." Thesis, Linnéuniversitetet, Institutionen för kulturvetenskaper (KV), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105759.

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Анотація:

The paper reports findings from a FAIR principles-based research project dedicated to investigating how cross-field research between the DH and Sociotechnical Sustainable Transport Research could help to enhance the holistic understanding of sociotechnical low-carbon transport transitions. Using the hashtag search queries #sustainabletransport and #sustainablemobility, 33,121 Tweets (2013-2021) and 8,089 Instagram images including captions (2017/2018-2021) were mined using Python scripts. Quantitative text and sentiment analyses were applied to the Tweets and image captions. Additionally, an

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10

Aarflot, Markus, and Pontus Jangstam. "Future Logistical Services from Connected Vehicles : A Case Study at Scania CV AB." Thesis, Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-64013.

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The road based transportation operations are growing rapidly, but the current infrastructure cannot sustain the entire growth. At the same time vehicle utilisation and fill rates are low. Improved efficiency of the operations is a necessary way forward for road based transportation. Parallel to this, heavy vehicle producers are currently improving the efficiency with services accompanying the product that are focused on the driver and the vehicle performance. However, the data from connected vehicles required for these services only entail a small amount of the operational data generated by co

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11

Alshaer, Mohammad. "An Efficient Framework for Processing and Analyzing Unstructured Text to Discover Delivery Delay and Optimization of Route Planning in Realtime." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1105/document.

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Анотація:

L'Internet des objets, ou IdO (en anglais Internet of Things, ou IoT) conduit à un changement de paradigme du secteur de la logistique. L'avènement de l'IoT a modifié l'écosystème de la gestion des services logistiques. Les fournisseurs de services logistiques utilisent aujourd'hui des technologies de capteurs telles que le GPS ou la télémétrie pour collecter des données en temps réel pendant la livraison. La collecte en temps réel des données permet aux fournisseurs de services de suivre et de gérer efficacement leur processus d'expédition. Le principal avantage de la collecte de données en t

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12

容勁 and King Stanley Yung. "Application of multi-agent technology to supply chain management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31223886.

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13

Lu, Yapeng, and 呂亞鵬. "An integrated algorithm for distributed optimization in networked systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224234.

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14

Bruzzone, Andrea. "P-SGLD : Stochastic Gradient Langevin Dynamics with control variates." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140121.

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Анотація:

Year after years, the amount of data that we continuously generate is increasing. When this situation started the main challenge was to find a way to store the huge quantity of information. Nowadays, with the increasing availability of storage facilities, this problem is solved but it gives us a new issue to deal with: find tools that allow us to learn from this large data sets. In this thesis, a framework for Bayesian learning with the ability to scale to large data sets is studied. We present the Stochastic Gradient Langevin Dynamics (SGLD) framework and show that in some cases its approxima

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15

Bohle, Alexander, and Liam Johnson. "Supply Chain Analytics implications for designing Supply Chain Networks : Linking Descriptive Analytics to operational Supply Chain Analytics applications to derive strategic Supply Chain Network Decisions." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Centre of Logistics and Supply Chain Management (CeLS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-44120.

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Анотація:

Today’s dynamic and increasingly competitive market had expanded complexities for global businesses pressuring companies to start leveraging on Big Data solutions in order to sustain the global competitions by becoming more data-driven in managing their supply chains.The main purpose of this study is twofold, 1) to explore the implications of applying analytics designing supply chain networks, 2) to investigate the link between operational and strategic management levels when making strategic decisions using Analytics.Qualitative methods have been applied for this study to gain a greater under

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16

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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17

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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Анотація:

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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18

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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Анотація:

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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19

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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20

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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Анотація:

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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21

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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22

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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Анотація:

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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23

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

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Анотація:

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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24

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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25

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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Анотація:

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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26

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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Анотація:

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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27

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

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Анотація:

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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28

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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29

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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Анотація:

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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30

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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Анотація:

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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31

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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Анотація:

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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32

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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Анотація:

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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33

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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Анотація:

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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34

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

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Анотація:

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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35

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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