Дисертації з теми "Big data with missingness"
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Cao, Yu. "Bayesian nonparametric analysis of longitudinal data with non-ignorable non-monotone missingness." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5750.
Повний текст джерелаHansen, 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.
Повний текст джерелаDeng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.
Повний текст джерелаTitle from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center
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.
Повний текст джерелаRizk, Raya. "Big Data Validation." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353850.
Повний текст джерелаJaber, 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.
Повний текст джерелаPresenting data in graphical forms is important in many different industries in order tounderstand information asset from data that is being collected. The amount of data is growingfast and brings new challenges for visualizing the data in graphical representations. Systemsare dependent on data visualization for detecting defects and faults of productions. Byimproved performance of time series data visualization increases the ability of detectingfaults and defects of productions.This report takes up a methods for visualizing time series data with high velocity in toaccount and discusses how big data of multivariable can be visualized with PCA.
Blahová, Leontýna. "Big Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203994.
Повний текст джерелаKämpe, 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.
Повний текст джерелаHafez, Mai. "Analysis of multivariate longitudinal categorical data subject to nonrandom missingness : a latent variable approach." Thesis, London School of Economics and Political Science (University of London), 2015. http://etheses.lse.ac.uk/3184/.
Повний текст джерелаAndersson, Oscar, and Tim Andersson. "AI applications on healthcare data." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44752.
Повний текст джерелаSherikar, Vishnu Vardhan Reddy. "I2MAPREDUCE: DATA MINING FOR BIG DATA." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/437.
Повний текст джерелаGiordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.
Повний текст джерелаFrancke, 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.
Повний текст джерелаSantos, Lúcio Fernandes Dutra. "Similaridade em big data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07022018-104929/.
Повний текст джерелаThe data being collected and generated nowadays increase not only in volume, but also in complexity, requiring new query operators. Health care centers collecting image exams and remote sensing from satellites and from earth-based stations are examples of application domains where more powerful and flexible operators are required. Storing, retrieving and analyzing data that are huge in volume, structure, complexity and distribution are now being referred to as big data. Representing and querying big data using only the traditional scalar data types are not enough anymore. Similarity queries are the most pursued resources to retrieve complex data, but until recently, they were not available in the Database Management Systems. Now that they are starting to become available, its first uses to develop real systems make it clear that the basic similarity query operators are not enough to meet the requirements of the target applications. The main reason is that similarity is a concept formulated considering only small amounts of data elements. Nowadays, researchers are targeting handling big data mainly using parallel architectures, and only a few studies exist targeting the efficacy of the query answers. This Ph.D. work aims at developing variations for the basic similarity operators to propose better suited similarity operators to handle big data, presenting a holistic vision about the database, increasing the effectiveness of the provided answers, but without causing impact on the efficiency on the searching algorithms. To achieve this goal, four mainly contributions are presented: The first one was a result diversification model that can be applied in any comparison criteria and similarity search operator. The second one focused on defining sampling and grouping techniques with the proposed diversification model aiming at speeding up the analysis task of the result sets. The third contribution concentrated on evaluation methods for measuring the quality of diversified result sets. Finally, the last one defines an approach to integrate the concepts of visual data mining and similarity with diversity searches in content-based retrieval systems, allowing a better understanding of how the diversity property is applied in the query process.
Francke, Angela, and Sven Lißner. "Big Data im Radverkehr." Technische Universität Dresden, 2017. https://tud.qucosa.de/id/qucosa%3A29637.
Повний текст джерелаВиноградова, О. В. "Використання Big Data компаніями". Thesis, Київський національний універститет технологій та дизайну, 2017. https://er.knutd.edu.ua/handle/123456789/10417.
Повний текст джерелаBlaho, Matúš. "Aplikace pro Big Data." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385977.
Повний текст джерелаFlike, 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.
Повний текст джерелаSánchez, Adam. "Big Data, Linked Data y Web semántica." Universidad Peruana de Ciencias Aplicadas (UPC), 2016. http://hdl.handle.net/10757/620705.
Повний текст джерелаConferencia que aborda aspectos del protocolo Linked Data, temas de Big Data y Web Semantica,
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.
Повний текст джерелаBig data är ett koncept som växer snabbt. När mer och mer data genereras och samlas in finns det ett ökande behov av effektiva lösningar som kan användas föratt behandla all denna data, i försök att utvinna värde från den. Syftet med detta examensarbete är att hitta ett effektivt sätt att snabbt behandla ett stort antal filer, av relativt liten storlek. Mer specifikt så är det för att testa två ramverk som kan användas vid big data-behandling. De två ramverken som testas mot varandra är Apache NiFi och Apache Storm. En metod beskrivs för att, för det första, konstruera ett dataflöde och, för det andra, konstruera en metod för att testa prestandan och skalbarheten av de ramverk som kör dataflödet. Resultaten avslöjar att Apache Storm är snabbare än NiFi, på den typen av test som gjordes. När antalet noder som var med i testerna ökades, så ökade inte alltid prestandan. Detta visar att en ökning av antalet noder, i en big data-behandlingskedja, inte alltid leder till bättre prestanda och att det ibland krävs andra åtgärder för att öka prestandan.
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.
Повний текст джерелаCao, Yang. "Querying big data with bounded data access." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/25421.
Повний текст джерелаAl-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.
Повний текст джерелаThe 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 Petabytes stored in the world in 2000. Today’s internet has about 0.1 Zettabytes of data (ZB is about 1021 bytes), and this number will reach 35 ZB by 2020. With such an overwhelming flood of information, present data management systems are not able to scale to this huge amount of raw, unstructured data—in today’s parlance, Big Data. In the present study, we show the basic concepts and design of Big Data tools, algorithms, and techniques. We compare the classical data mining algorithms to the Big Data algorithms by using Hadoop/MapReduce as a core implementation of Big Data for scalable algorithms. We implemented the K-means algorithm and A-priori algorithm with Hadoop/MapReduce on a 5 nodes Hadoop cluster. We explore NoSQL databases for semi-structured, massively large-scaling of data by using MongoDB as an example. Finally, we show the performance between HDFS (Hadoop Distributed File System) and MongoDB data storage for these two algorithms.
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.
Повний текст джерелаGame Analytics is a research field that appeared recently. Game developers have the ability to analyze how customers use their products down to every button pressed. This can result in large amounts of data and the challenge is to make sense of it all. The challenges with game data is often described with the same characteristics used to define Big Data: volume, velocity and variability. This should mean that there is potential for a fruitful collaboration. The purpose of this study is to analyze and evaluate what possibilities Big Data has to develop the Game Analytics field. To fulfill this purpose a literature review and semi-structured interviews with people active in the gaming industry were conducted. The results show that the sources agree that valuable information can be found within the data you can store, especially in the monetary, general and core values to the specific game. With more advanced analysis you may find other interesting patterns as well but nonetheless the predominant way seems to be sticking to the simple variables and staying away from digging deeper. It is not because data handling or storing would be tedious or too difficult but simply because the analysis would be too risky of an investment. Even if you have someone ready to take on all the challenges game data sets up, there is not enough trust in the answers or how useful they might be. Visions of the future within the field are very modest and the nearest future seems to hold mostly efficiency improvements and a widening of the field, making it reach more people. This does not really post any new demands or requirements on the data handling.
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.
Повний текст джерелаDoucet, 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.
Повний текст джерелаThe 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 utilize the underlying open-source tools for big data analytics. A virtualized cloud environment was built and analyzed at the Naval Postgraduate School, which offers a test bed, suitable for studying novel variations of these architectures. Further, the technologies directly used to implement the test bed seek to demonstrate a sustainable methodology for rapidly configuring and deploying virtualized machines and provides an environment for performance benchmark and testing. The capstone findings indicate the strategies and best practices to automate the deployment, provisioning and management of big data clusters. The functionality we seek to support is a far more general goal: finding open-source tools that help to deploy and configure large clusters for on-demand big data analytics.
Lansley, Guy David. "Big data : geodemographics and representation." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10045119/.
Повний текст джерелаCao, Lei. "Outlier Detection In Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/82.
Повний текст джерелаTalbot, David. "Bloom maps for big data." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/25235.
Повний текст джерелаRupprecht, Lukas. "Network-aware big data processing." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/52455.
Повний текст джерелаAndersson, 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.
Повний текст джерелаСлишинська, В. О., та Ігор Віталійович Пономаренко. "Використання Big Data в маркетингу". Thesis, КНУТД, 2016. https://er.knutd.edu.ua/handle/123456789/4082.
Повний текст джерелаПанферова, И. Ю. "Анализ неструктурированных данных big data". Thesis, Академія внутрішніх військ МВС України, 2017. http://openarchive.nure.ua/handle/document/9973.
Повний текст джерелаLuo, 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.
Повний текст джерелаŠoltýs, Matej. "Big Data v technológiách IBM." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-193914.
Повний текст джерелаMiloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.
Повний текст джерелаAl-Salim, Ali Mahdi Ali. "Energy efficient big data networks." Thesis, University of Leeds, 2018. http://etheses.whiterose.ac.uk/20640/.
Повний текст джерелаNeagu, Daniel, and A.-N. Richarz. "Big data in predictive toxicology." Royal Society of Chemistry, 2019. http://hdl.handle.net/10454/17603.
Повний текст джерелаThe 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 opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment.
Potter, Justin Gregory. "Big data adoption in SMMEs." Diss., University of Pretoria, 2015. http://hdl.handle.net/2263/52297.
Повний текст джерелаMini Dissertation (MBA)--University of Pretoria, 2015.
sn2016
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
Mai, Luo. "Towards efficient big data processing in data centres." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/64817.
Повний текст джерелаChitondo, Pepukayi David Junior. "Data policies for big health data and personal health data." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.
Повний текст джерелаHealth information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, even more so in the healthcare field where the accuracy of data is life critical. How big health data can be used to achieve improved health is now the goal of the current health informatics practitioner. Even more exciting is the amount of health data being generated by patients via personal handheld devices and other forms of technology that exclude the healthcare practitioner. This patient-generated data is also known as Personal Health Records, PHR. To achieve meaningful use of PHRs and healthcare data in general through big data, a couple of hurdles have to be overcome. First and foremost is the issue of privacy and confidentiality of the patients whose data is in concern. Secondly is the perceived trustworthiness of PHRs by healthcare practitioners. Other issues to take into context are data rights and ownership, data suppression, IP protection, data anonymisation and reidentification, information flow and regulations as well as consent biases. This study sought to understand the role of data policies in the process of data utilisation in the healthcare sector with added interest on PHRs utilisation as part of big health data.
Kalibjian, Jeff. ""Big Data" Management and Security Application to Telemetry Data Products." International Foundation for Telemetering, 2013. http://hdl.handle.net/10150/579664.
Повний текст джерела"Big Data" [1] and the security challenge of managing "Big Data" is a hot topic in the IT world. The term "Big Data" is used to describe very large data sets that cannot be processed by traditional database applications in "tractable" periods of time. Securing data in a conventional database is challenge enough; securing data whose size may exceed hundreds of terabytes or even petabytes is even more daunting! As the size of telemetry product and telemetry post-processed product continues to grow, "Big Data" management techniques and the securing of that data may have ever increasing application in the telemetry realm. After reviewing "Big Data", "Big Data" security and management basics, potential application to telemetry post-processed product will be explored.
Grohsschmiedt, Steffen. "Making Big Data Smaller : Reducing the storage requirements for big data with erasure coding for Hadoop." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177201.
Повний текст джерелаRystadius, Gustaf, David Monell, and Linus Mautner. "The dynamic management revolution of Big Data : A case study of Åhlen’s Big Data Analytics operation." Thesis, Jönköping University, Internationella Handelshögskolan, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48959.
Повний текст джерелаSerra-Diaz, Josep M., Brian J. Enquist, Brian Maitner, Cory Merow, and Jens-C. Svenning. "Big data of tree species distributions: how big and how good?" SPRINGER HEIDELBERG, 2018. http://hdl.handle.net/10150/626611.
Повний текст джерелаBishop, Brenden. "Examining Random-Coeffcient Pattern-Mixture Models forLongitudinal Data with Informative Dropout." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu150039066582153.
Повний текст джерелаMcCaul, Christopher Francis. "Big Data: Coping with Data Obesity in Cloud Environments." Thesis, Ulster University, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.724751.
Повний текст джерелаBernsdorf, Bodo, and Julian Bruns. "Big Data und Data-Mining im Umfeld städtischer Nutzungskartierung." Rhombos-Verlag, 2016. https://slub.qucosa.de/id/qucosa%3A16835.
Повний текст джерелаFranceschini, Davide. "Panoramica sull'utilizzo etico dei Big Data." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13809/.
Повний текст джерелаLiu, Yang. "Statistical methods for big tracking data." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/60916.
Повний текст джерелаScience, Faculty of
Statistics, Department of
Graduate