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Teses / dissertações sobre o tema "Cloud-based big data analytics"

Autor: Grafiati

Publicado: 7 de junho de 2025

Última modificação: 2 de agosto de 2025

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1

Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

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This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies

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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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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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Rashid, A. N. M. Bazlur. "Cooperative co-evolution-based feature selection for big data analytics." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2428.

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The rapid progress of modern technologies generates a massive amount of highthroughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (attributes). However, irrelevant features may degrade the classification performance of ML algorithms. Feature selection (FS) is a combinatorial optimisation technique used to select a subset of relevant features that represent the dataset. For example, FS is an effective preprocessing step of anomaly detection techniques in Big Cybersecurity Datasets. Evolutiona

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Sellén, David. "Big Data analytics for the forest industry : A proof-of-conceptbuilt on cloud technologies." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-28541.

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Large amounts of data in various forms are generated at a fast pace in today´s society. This is commonly referred to as “Big Data”. Making use of Big Data has been increasingly important for both business and in research. The forest industry is generating big amounts of data during the different processes of forest harvesting. In Sweden, forest infor-mation is sent to SDC, the information hub for the Swedish forest industry. In 2014, SDC received reports on 75.5 million m3fub from harvester and forwarder machines. These machines use a global stand-ard called StanForD 2010 for communication and

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Olsén, Cleas, and Gustav Lindskog. "Big Data Analytics : A potential way to Competitive Performance." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104372.

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Big data analytics (BDA) has become an increasingly popular topic over the years amongst academics and practitioners alike. Big data, which is an important part of BDA, was originally defined with three Vs, being volume, velocity and variety. In later years more Vs have surfaced to better accommodate the current need. The analytics of BDA consists of different methods of analysing gathered data. Analysing data can provide insights to organisations which in turn can give organisations competitive advantage and enhance their businesses. Looking into the necessary resources needed to build big da

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Denadija, Feda, and David Löfgren. "Revealing the Non-technical Side of Big Data Analytics : Evidence from Born analyticals and Big intelligent firms." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298137.

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This study aspired to gain a more a nuanced understanding of the emerging analytics technologies and the vital capabilities that ultimately drive evidence-based decision making. Big data technology is widely discussed by varying groups in society and believed to revolutionize corporate decision making. In spite of big data's promising possibilities only a trivial fraction of firms deploying big data analytics (BDA) have gained significant benefits from their initiatives. Trying to explain this inability we leaned back on prior IT literature suggesting that IT resources can only be successfully

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Barros, Victor Perazzolo. "Big data analytics em cloud gaming: um estudo sobre o reconhecimento de padrões de jogadores." Universidade Presbiteriana Mackenzie, 2017. http://tede.mackenzie.br/jspui/handle/tede/3405.

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Submitted by Rosa Assis (rosa_assis@yahoo.com.br) on 2017-11-14T18:05:03Z No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Approved for entry into archive by Paola Damato (repositorio@mackenzie.br) on 2017-11-27T12:14:38Z (GMT) No. of bitstreams: 2 VICTOR PERAZZOLO BARROS.pdf: 24134660 bytes, checksum: 8761000aa9ba093f81a14b3c2368f2b7 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)<br>Made available in DSpace on 2017-11-27T12:1

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Ceccarello, Matteo. "Clustering-based Algorithms for Big Data Computations." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3424849.

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In the age of big data, the amount of information that applications need to process often exceeds the computational capabilities of single machines. To cope with this deluge of data, new computational models have been defined. The MapReduce model allows the development of distributed algorithms targeted at large clusters, where each machine can only store a small fraction of the data. In the streaming model a single processor processes on-the-fly an incoming stream of data, using only limited memory. The specific characteristics of these models combined with the necessity of processing ver

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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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Serrano, Antonio Manuel Rubio. "Contributions on BI systems based on big data and predictive analytics integrated with an etl process." reponame:Repositório Institucional da UnB, 2014. http://repositorio.unb.br/handle/10482/18208.

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Dissertação (mestrado)— Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2014.<br>Submitted by Ana Cristina Barbosa da Silva (annabds@hotmail.com) on 2015-03-27T18:26:58Z No. of bitstreams: 1 2014_AntonioManuelRubioSerrano.pdf: 2936232 bytes, checksum: 2f60301c6aa9536e02021ad389644ed6 (MD5)<br>Approved for entry into archive by Guimaraes Jacqueline(jacqueline.guimaraes@bce.unb.br) on 2015-05-19T13:52:40Z (GMT) No. of bitstreams: 1 2014_AntonioManuelRubioSerrano.pdf: 2936232 bytes, checksum: 2f60301c6aa9536e02021ad389644ed6 (MD5)<br>Made available in DSpac

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Velthuis, Paul. "New authentication mechanism using certificates for big data analytic tools." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-215694.

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Companies analyse large amounts of sensitive data on clusters of machines, using a framework such as Apache Hadoop to handle inter-process communication, and big data analytic tools such as Apache Spark and Apache Flink to analyse the growing amounts of data. Big data analytic tools are mainly tested on performance and reliability. Security and authentication have not been enough considered and they lack behind. The goal of this research is to improve the authentication and security for data analytic tools.Currently, the aforementioned big data analytic tools are using Kerberos for authenticat

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Kapitán, Lukáš. "Vliv vývojových trendů na řešení projektu BI." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-150006.

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The aim of this these is to analyse the trends occurring in Business intelligence. It does examine, summarise and judge each of the trends from the point of their usability in the real world, their influence and modification of each phase of the implementation of Bussiness intelligence. It is clear that each of these trends has its positives and negatives which can influence the statements in the evaluation. These factors are taken into consideration and analysed as well. The advantages and disadvantages of the trends are occurring especially in the areas of economical demand and technical dif

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Galletti, Alessandro, and Dimitra-Christina Papadimitriou. "How Big Data Analytics are perceived as a driver for Competitive Advantage : A qualitative study on food retailers." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-205508.

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The recent explosion of digital data has led the business world to a new era towards a more evidence-based decision making. Companies nowadays collect, store and analyze huge amount of data and the terms such Big Data Analytics are used to define those practices. This paper investigates how Big Data Analytics (BDA) can be perceived and used as a driver for companies’ Competitive Advantage (CA). It thus contributes in the debate about the potential role of IT assets as a source of CA, through a Resource-Based View approach, by introducing a new phenomenon such as BDA in that traditional theoret

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Fasano, Domenico. "progettazione e sviluppo in una enterprise data platform per l’analisi di fondi strutturali: il caso regione emilia-romagna." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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Uno dei clienti di Iconsulting è la Regione Emilia-Romagna, con la quale vengono realizzati diversi progetti nell'ottica della Data Analytics, tra cui il progetto denominato DatalakER al quale ho contribuito, nato per mettere a disposizione della pubblica amministrazione regionale uno strumento più evoluto di quelli precedentemente esistenti per l'analisi del patrimonio informativo a loro disposizione. La soluzione individuata è stata quella di creare una Data Platform con una architettura adatta ad un contesto Enterprise, in cui sono priorità critiche la sicurezza e la corretta gestione dei d

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MALAVISI, MARZIA. "Structural Health Monitoring Framework for Automatic Damage Detection based on IoT and Big Data Analytics: Application to a network of structures." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2840369.

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Coimbra, Rafael Melo. "Framework based on lambda architecture applied to IoT: case scenario." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21739.

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Mestrado em Sistemas de Informação<br>Desde o início da primeira década do presente milénio, tem-se testemunhado um aumento exponencial da quantidade de dados produzidos de dia para dia. Numa primeira instância, o aumento foi atribuído aos dados gerados pelos dispositivos GPS; numa segunda fase, à rápida expansão das redes sociais, agora não devido a um fator específico, mas devido ao surgimento de um novo conceito denominado de Internet das Coisas. Este novo conceito, com resultados já mensuráveis, nasceu da premissa de facilitar o dia-a-dia das pessoas fazendo com que os dispositivos eletró

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Fageehi, Yahya. "SIMULATION-BASED OPTIMIZATION FOR COMPLEX SYSTEMS WITH SUPPLY AND DEMAND UNCERTAINTY." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1531147903589262.

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Zheng, Yao. "Privacy Preservation for Cloud-Based Data Sharing and Data Analytics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73796.

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Data privacy is a globally recognized human right for individuals to control the access to their personal information, and bar the negative consequences from the use of this information. As communication technologies progress, the means to protect data privacy must also evolve to address new challenges come into view. Our research goal in this dissertation is to develop privacy protection frameworks and techniques suitable for the emerging cloud-based data services, in particular privacy-preserving algorithms and protocols for the cloud-based data sharing and data analytics services. Cloud co

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Islam, Md Zahidul. "A Cloud Based Platform for Big Data Science." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-103700.

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With the advent of cloud computing, resizable scalable infrastructures for data processing is now available to everyone. Software platforms and frameworks that support data intensive distributed applications such as Amazon Web Services and Apache Hadoop enable users to the necessary tools and infrastructure to work with thousands of scalable computers and process terabytes of data. However writing scalable applications that are run on top of these distributed frameworks is still a demanding and challenging task. The thesis aimed to advance the core scientific and technological means of managin

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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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Reuterswärd, John. "Implementation & architecture of a cloud-based data analytics pipeline." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301335.

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Organizations can gain deeper insight into the usage of their products and services by analyzing data from events as they happen in real-time. Examples of such data include user login attempts, feature usage, sign-ups, client logs as well as other domain and application specific events. The high frequency at which these types of events are generated combined with the large volume of data they will occupy once stored provides many interesting challenges. Most importantly, it is difficult to predict what type of analytics data will provide useful insights in the future. This master thesis docume

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Pisapia, Claudio. "SAP HANA Energy Management System: creazione di un prototipo di business in Syskoplan Reply." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Il presente lavoro di tesi verterà sulla creazione di un’Energy Management System in grado di soddisfare le esigenze delle imprese sia lato manufacturing che utilities. Nel primo capitolo oltre ad un‘introduzione al tema dell’EMS, verrà descritta la figura dell’Energy manager e fatta una panoramica sulla parte normativa in merito, cioè la ISO 50001 e la direttiva 2012/27/UE. Nel secondo capitolo verrà rimarcata l’importanza dei dati, in particolare verranno descritti nel dettaglio i temi della Business Intelligence, i Big Data, l’Internet of Things e la Predictive Analytics. Nel terzo capit

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Martins, Pedro Miguel Pereira Serrano. "Evaluation and optimization of a session-based middleware for data management." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12609.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>The current massive daily production of data has created a non-precedent opportunity for information extraction in many domains. However, this huge rise in the quantities of generated data that needs to be processed, stored, and timely delivered, has created several new challenges. In an effort to attack these challenges [Dom13] proposed a middleware with the concept of a Session capable of dynamically aggregating, processing and disseminating large amounts of data to groups of clients, depending on their interests.

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Khaleel, Ali. "Optimisation of a Hadoop cluster based on SDN in cloud computing for big data applications." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17076.

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Big data has received a great deal attention from many sectors, including academia, industry and government. The Hadoop framework has emerged for supporting its storage and analysis using the MapReduce programming module. However, this framework is a complex system that has more than 150 parameters and some of them can exert a considerable effect on the performance of a Hadoop job. The optimum tuning of the Hadoop parameters is a difficult task as well as being time consuming. In this thesis, an optimisation approach is presented to improve the performance of a Hadoop framework by setting the

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Runsewe, Olubisi A. "A Novel Cloud Broker-based Resource Elasticity Management and Pricing for Big Data Streaming Applications." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39251.

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The pervasive availability of streaming data from various sources is driving todays’ enterprises to acquire low-latency big data streaming applications (BDSAs) for extracting useful information. In parallel, recent advances in technology have made it easier to collect, process and store these data streams in the cloud. For most enterprises, gaining insights from big data is immensely important for maintaining competitive advantage. However, majority of enterprises have diﬃculty managing the multitude of BDSAs and the complex issues cloud technologies present, giving rise to the incorporation o

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Sellami, Rami. "Supporting multiple data stores based applications in cloud environments." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL002/document.

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Avec l’avènement du cloud computing et des big data, de nouveaux systèmes de gestion de bases de données sont apparus, connus en général sous le vocable systèmes NoSQL. Par rapport aux systèmes relationnels, ces systèmes se distinguent par leur absence de schéma, une spécialisation pour des types de données particuliers (documents, graphes, clé/valeur et colonne) et l’absence de langages de requêtes déclaratifs. L’offre est assez pléthorique et il n’y a pas de standard aujourd’hui comme peut l’être SQL pour les systèmes relationnels. De nombreuses applications peuvent avoir besoin de manipuler

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Muthiah, Karthika Ms. "Performance Evaluation of Hadoop based Big Data Applications with HiBench Benchmarking tool on IaaS Cloud Platforms." UNF Digital Commons, 2017. https://digitalcommons.unf.edu/etd/771.

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Cloud computing is a computing paradigm where large numbers of devices are connected through networks that provide a dynamically scalable infrastructure for applications, data and storage. Currently, many businesses, from small scale to big companies and industries, are changing their operations to utilize cloud services because cloud platforms could increase company’s growth through process efficiency and reduction in information technology spending [Coles16]. Companies are relying on cloud platforms like Amazon Web Services, Google Compute Engine, and Microsoft Azure, etc., for their busines

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Zapata, Gianpierre, Javier Murga, Carlos Raymundo, Jose Alvarez, and Francisco Dominguez. "Predictive model based on sentiment analysis for peruvian smes in the sustainable tourist sector." SciTePress, 2017. http://hdl.handle.net/10757/656350.

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In the sustainable tourist sector today, there is a wide margin of loss in small and medium-sized enterprise (SMEs) because of a poor control in logistical expenses. In other words, acquired goods are note being sold, a scenario which is very common in tourism SMEs. These SMEs buy a number of travel packages to big companies and because of the lack of demand of said packages, they expire and they become an expense, not the investment it was meant to be. To solve this problem, we propose a Predictive model based on sentiment analysis of a social networks that will help the sales decision making

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Kashyap, Pradyumna Krishna. "Project-based Multi-tenant Container Registry For Hopsworks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284561.

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There has been a substantial growth in the usage of data in the past decade, cloud technologies and big data platforms have gained popularity as they help in processing such data on a large scale. Hopsworks is such a managed plat- form for scale out data science. It is an open-source platform for the develop- ment and operation of Machine Learning models, available on-premise and as a managed platform in the cloud. As most of these platforms provide data sci- ence environments to collate the required libraries to work with, Hopsworks provides users with Anaconda environments.Hopsworks provides

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Bornschlegl, Marco X. [Verfasser], Matthias [Akademischer Betreuer] Hemmje, Matthias [Gutachter] Hemmje, and Paolo [Gutachter] Buono. "Advanced Visual Interfaces Supporting Distributed Cloud-Based Big Data Analysis / Marco X. Bornschlegl ; Gutachter: Matthias Hemmje, Paolo Buono ; Betreuer: Matthias Hemmje." Hagen : FernUniversität in Hagen, 2019. http://d-nb.info/1182680356/34.

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Arres, Billel. "Optimisation des performances dans les entrepôts distribués avec Mapreduce : traitement des problèmes de partionnement et de distribution des données." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE2012.

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Dans ce travail de thèse, nous abordons les problèmes liés au partitionnement et à la distribution des grands volumes d’entrepôts de données distribués avec Mapreduce. Dans un premier temps, nous abordons le problème de la distribution des données. Dans ce cas, nous proposons une stratégie d’optimisation du placement des données, basée sur le principe de la colocalisation. L’objectif est d’optimiser les traitements lors de l’exécution des requêtes d’analyse à travers la définition d’un schéma de distribution intentionnelle des données permettant de réduire la quantité des données transférées e

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STRIGARO, DANIELE. "Management and protection of lake ecosystems by enabling data analytics based on a cost-effective, open and high-frequency monitoring system." Doctoral thesis, Università degli studi di Pavia, 2023. https://hdl.handle.net/11571/1469885.

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Homem, Irvin. "LEIA: The Live Evidence Information Aggregator : A Scalable Distributed Hypervisor‐based Peer‐2‐Peer Aggregator of Information for Cyber‐Law Enforcement I." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177902.

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The Internet in its most basic form is a complex information sharing organism. There are billions of interconnected elements with varying capabilities that work together supporting numerous activities (services) through this information sharing. In recent times, these elements have become portable, mobile, highly computationally capable and more than ever intertwined with human controllers and their activities. They are also rapidly being embedded into other everyday objects and sharing more and more information in order to facilitate automation, signaling that the rise of the Internet of Thin

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Zeng, Xuezhi. "Management of Service Level Agreements for Big Data Analytics Applications in Cloud: A Layer-based Study." Phd thesis, 2019. http://hdl.handle.net/1885/179544.

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Nowadays, deployment and operation of big data analytics applications (BDAAs) in cloud are increasingly becoming a trending practice. These applications offer organizations the capabilities of constructing valuable information and extracting actionable insight for enhancing the evidence-based decision-making process. Many leading providers such as Google and Amazon provision such analytics capabilities in the form of service to customers in a pay-per-use economic model. In today's competitive world, the potential business values of these applications depend a lot on the quality of service offe

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Hsu, Ching-Heng, and 徐清亨. "Edge Computing Using Fog Technology for Big Data Analytics in the Cloud." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/t26q3d.

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碩士<br>中原大學<br>資訊管理研究所<br>105<br>In recent years, the rapid growth of the internet data information which cause the big-data analysis has become a very important business model and the sources of the profit; however, under the cloud computing architecture, Hadoop running the MapReduce action will use huge amount of the network bandwidth, especially in more nodes case. It has serious impact for the quality of the network transmission. The purpose of this paper will explain how to use the MapReduce as the base and combine with fog operations(Fog Computing) Data pre-processing capacity to forward

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Lai, Ruei-Yang, and 賴瑞陽. "Ontology based Big Data Analytics for the Benchmarking Hospitality Industry." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/86094624228790627954.

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碩士<br>元智大學<br>工業工程與管理學系<br>105<br>Hospitality industry is a data rich industry that captures huge volumes of data of various types including arrival time, the frequency of use of public facilities, meals diet, customer service, social network comments, etc. with high velocity. These data encapsulate useful information regarding every phase of the customer journey. The effective use of analytics can improve dramatically how business is run in terms of delivering memorable and personalized guest experiences, while maximizing revenue and profits. The challenge of Big Data doesn't just stem from t

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Kommineni, J., and Krishna Mohan C. "Content Based Image Retrieval by Preprocessing Image Database." Thesis, 2011. http://raiith.iith.ac.in/597/1/CS09G002.pdf.

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Increase in communication bandwidth, information content and the size of the multimedia databases have given rise to the concept of Content Based Image Retrieval (CBIR). Content based image retrieval is a technique that enables a user to extract similar images based on a query, from a database containing a large amount of images. A basic issue in designing a content based image retrieval system is to select the image features that best represent image content in a database. Current research in this area focuses on improving image retrieval accuracy. In this work, we have presented an eci

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Adepu, R. S. "Temporal Coherence in Energy-based Deep Learning Machines for Action Recognition." Thesis, 2014. http://raiith.iith.ac.in/112/1/CS11M03.pdf.

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Deep Learning, a sub-area of machine learning, has become a buzz word in recent days due to its great successes in many applications of machine learning, including speech processing, computer vision and natural language processing. Deep learning became famous in the initial days through the successful application of Convolutional Neural Networks as well as Energy-based Models -or Restricted Boltzmann Machines (RBMs) - on handwritten digit recognition. While the last decade has seen the growing use of convolution-based deep learning methods for image analysis, limited work has been done i

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Kamath, N., and Bheemarjuna Reddy Tamma. "IP Flow Mobility based Offload in LTE Wi-Fi Interworking Scenario." Thesis, 2014. http://raiith.iith.ac.in/971/1/CS11M02.pdf.

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Mobile data traffic has seen an exponential growth in the past few years with the trend expected to continue. LTE as a standalone cellular network is unable to keep pace with the increasing traffic demands. In the meanwhile, wireless LAN has proven itself as an economical wireless access technology. 3GPP has thus been encouraged to standardize the integration of Wi-Fi networks with LTE. This opens up numerous opportunities to study data offloading and mobility management protocols. One of the newer offloading technique is known as IP Flow Mobility, where individual IP flows are migrated

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Fan, Yu-Neng, and 范有寧. "A Novel Rough Set-based Rule Induction for Big Data Analytics." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/95010008504476603845.

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博士<br>國立臺灣大學<br>資訊管理學研究所<br>101<br>Rough set-based rule induction is able to generate decision rules from a database and has mechanisms to handle noise and uncertainty in data. Using these meaningful decision rules, the technique facilitates managerial decision-making. However, databases are used to run the day-to-day operations of a business must process quickly. Large volumes of data are continually updatedwithin a short period of time. The infrastructure required to analyze such large amounts of data must be able to support a deeper analysis, to deal with extreme data volumes, to allow fast

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LIAO, PO-HAO, and 廖柏豪. "Deep Learning Based Integration and Optimization of Big Data Analytics Platforms." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/349jtp.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>106<br>This study focused on big data analysis job scheduling mechanism, predicting the time for big data analysis based on deep learning DNN (Deep Neural Network), and shortening the average waiting time of overall work by intelligent scheduling optimization. The proposed mechanism is expected to enhance the execution efficiency of big data analysis platform greatly. A multi-platform big data processing system, characterized by high efficiency, high availability and high expandability, is integrated with Hadoop and Spark to make the platform support R command-base

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Lin, Ching-Yi, and 林靜怡. "Application of Big Data Analytics in Telecom Industry Based On BroadBand Log." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/97914825195555360815.

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碩士<br>國立臺灣科技大學<br>管理研究所<br>101<br>In recent years, big data has become a critical issue, driven by the popularity of smart phones and social media. Technology venders increase its investment in big data analysis industry. Besides, many companies began to invest money and human resource to estimate the benefit that big data can bring in. By analyzing the data from customers’ behavior to improve the hit rate of cross-sell and up-sell and generate more profits. The company which understanding more own customers can take more advantages in the competition. By using text mining to analyze unstructu

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Huang, Yen Chun, and 黃彥鈞. "Developing Big Data Analytics Framework for Competency-Based Pay and Empirical Study." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/30038459268607410555.

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碩士<br>國立清華大學<br>工業工程與工程管理學系<br>104<br>Compensation experts advocate that competency-based pay can represent employee's job capabilities better than job-based pay. However, competencies exist complex relationships among each other. This leads to that competency-based pay have no formal and systematic evaluation process, and there is no common standard among different corporate departments. The study aims to develop a big data analytics framework for competency-based pay. The framework integrates stepwise regression, Random Forests, and Bayesian Network method to construct a competency analytics

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Wu, Meng-Yuan, and 吳孟遠. "A New Web Visitor Behavior Analysis Based on Big Data Analytics Techniques." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/343rte.

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碩士<br>國立東華大學<br>資訊工程學系<br>106<br>Many big data researches and discussions have proposed various analysis methods for big data from various sources. In the paper published by Professor Yung, the web log data has been officially proved to be big data. In this thesis, we use big data analysis technology to analyze the web log data, and develop a new behavior analysis for web visitors. According to the discussion of Professors Li, Yuan and Yung, web visitors can be classified into frequent visitors and incidental visitors; activity promotion can bring more new incidental visitors to the websites.

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Garofalo, Mauro. "Big Data Analytics for Flow-based Anomaly Detection in High-Speed Networks." Tesi di dottorato, 2017. http://www.fedoa.unina.it/11784/3/thesis.pdf.

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The Cisco VNI Complete Forecast Highlights clearly states that the Internet traffic is growing in three different directions, Volume, Velocity, and Variety, bringing computer network into the big data era. At the same time, sophisticated network attacks are growing exponentially. Such growth making the existing signature-based security tools, like firewall and traditional intrusion detection systems, ineffective against new kind of attacks or variations of known attacks. In this dissertation, we propose an unsupervised method for network anomaly detection. This method is able to detect unknown

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Jayaraman, P. P., C. Perera, D. Georgakopoulos, S. Dustdar, Dhaval Thakker, and R. Ranjan. "Analytics-as-a-Service in a Multi-Cloud Environment through Semantically-enabled Hierarchical Data Processing." 2016. http://hdl.handle.net/10454/8523.

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yes<br>A large number of cloud middleware platforms and tools are deployed to support a variety of Internet of Things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases intergation, cooperation, and can also lead to innovative use of the data. Multicloud, privacy-aware environments facilitate such data access, allowin

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YANG, SHU-YUN, and 楊淑雲. "Big Data Analytics and Customer-based Agility: With a Mediation of Market Knowledge." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/re2fsb.

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碩士<br>國立高雄師範大學<br>事業經營學系<br>105<br>Nowadays big data analytics has been getting popularity in practice and academic research. Its main purpose aims that, after applying big data analytics, organizations can not only further understand the market knowledge, but also flexibly react to the trend and change of market and environment. However, rare complete and systematic studies have paid efforts to cultivate the conducts and framework of business organizations analyzing big data as well as its benefits to the organizations. Hence, this study proposes to achieve the following three objectives:

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LIN, YE-ZE, and 林曄澤. "An Intelligent Constant-Humidity Irrigation System based on the Big Data Analytics: Design and Implementation." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/v4e2xe.

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碩士<br>國立虎尾科技大學<br>資訊工程系碩士班<br>107<br>In recent years, global climate anomalies have caused climate turmoil, and agriculture has become more difficult to survive under the influence of climate anomalies. Fortunately, with the advancement of science and technology and the popularization of the internet of things, there have been research systems designed to automatically irrigate systems, but many papers use uniform irrigation. The uniform irrigation may cause the soil humidity in some areas to be too high due to the topography. Therefore, this paper decided to design an intelligent constant-hum

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Liu, De-Chian, and 劉德謙. "Real-time Intelligent Health Care Demand Forecasting based on Big Data Predictive Analytics - A Case Study." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jv69xj.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>107<br>With the evolution of the information and communication technology, following the development of Industry 4.0, Artificial Intelligence (AI), Big Data, and Cloud Computing play important roles in the smart manufacturing factory. In this thesis, we focused on the big data predictive analytics, having three properties: velocity, variety and volume in healthcare management. In this thesis, we proposed a real time intelligent medical forecasting system, which was divided into two phases. In the first phase, a Big Data approach for Medical Demand Forecasting, includ

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