Дисертації з теми "Big Data analytics applications"
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Al-Shiakhli, Sarah. "Big Data Analytics: A Literature Review Perspective." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74173.
Повний текст джерелаTalevi, 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/.
Повний текст джерелаAbounia, Omran Behzad. "Application of Data Mining and Big Data Analytics in the Construction Industry." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934.
Повний текст джерелаZhang, Liangwei. "Big Data Analytics for Fault Detection and its Application in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60423.
Повний текст джерелаGreen, Oded. "High performance computing for irregular algorithms and applications with an emphasis on big data analytics." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51860.
Повний текст джерелаSvenningsson, Philip, and Maximilian Drubba. "How to capture that business value everyone talks about? : An exploratory case study on business value in agile big data analytics organizations." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48882.
Повний текст джерелаZubar, 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.
Повний текст джерелаBig 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 optimize routing, simplify factory functions and give transparency to the entire supply chain, from which both logistics companies and shipping companies may benefit. The third-party logistical company and a transportation company may agree on this issue. Though big data require a large number of high-quality information sources to work effectively.
Великі дані зробили революцію в багатьох сферах бізнесу, включаючи логістику та бізнес-процеси в ній. Складність та динаміка логістики в поєднанні з опорою на багато рухомих частин можуть спричинити вузькі місця у будь-якій точці ланцюга поставок, роблячи застосування великих даних важливим елементом ефективності у проектуванні та управлінні логістичними процесами. Наприклад, логістика великих даних може бути використана для оптимізації маршрутизації, спрощення заводських функцій та надання прозорості усьому ланцюжку поставок, від чого можуть виграти як логістичні компанії, так і судноплавні компанії. Стороння логістична компанія та транспортна компанія можуть домовитись щодо цього питання. Хоча великі дані вимагають великої кількості високоякісних джерел інформації для ефективної роботи.
Cui, Henggang. "Exploiting Application Characteristics for Efficient System Support of Data-Parallel Machine Learning." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/908.
Повний текст джерелаSharma, Rahil. "Shared and distributed memory parallel algorithms to solve big data problems in biological, social network and spatial domain applications." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2277.
Повний текст джерелаMatteuzzi, Tommaso. "Network diffusion methods for omics big bio data analytics and interpretation with application to cancer datasets." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13660/.
Повний текст джерелаBohle, 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.
Повний текст джерелаTosson, Amir [Verfasser], and Ullrich [Gutachter] Pietsch. "The way to a smarter community: exploring and exploiting data modeling, big data analytics, high-performance computing and artificial intelligence techniques for applications of 2D energy-dispersive detectors in the crystallography community / Amir Tosson ; Gutachter: Ullrich Pietsch." Siegen : Universitätsbibliothek der Universität Siegen, 2020. http://d-nb.info/1216332282/34.
Повний текст джерелаOskar, Marko. "Application of innovative methods of machine learning in Biosystems." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=108729&source=NDLTD&language=en.
Повний текст джерелаПредмет истраживања докторске дисертације је примена машинског учења у решавању проблема карактеристичних за биосистемe са нагласком на пољопривреду. Најпре је представљен иновативни алгоритам за регресију који је примењен на великој количини података како би се са предиковали приноси. На основу предикција одабране су одговарајуће сорте соје за њиве са одређеним карактеристикама унапређеним алгоритмом оптимизације портфолија. Напослетку је постављен оптимизациони проблем одређивања сетвене структуре са вишеструким функцијама циља који је решен иновативном методом, категоричким еволутивним алгоритмом заснованом на NSGA-III алгоритму.
Predmet istraživanja doktorske disertacije je primena mašinskog učenja u rešavanju problema karakterističnih za biosisteme sa naglaskom na poljoprivredu. Najpre je predstavljen inovativni algoritam za regresiju koji je primenjen na velikoj količini podataka kako bi se sa predikovali prinosi. Na osnovu predikcija odabrane su odgovarajuće sorte soje za njive sa određenim karakteristikama unapređenim algoritmom optimizacije portfolija. Naposletku je postavljen optimizacioni problem određivanja setvene strukture sa višestrukim funkcijama cilja koji je rešen inovativnom metodom, kategoričkim evolutivnim algoritmom zasnovanom na NSGA-III algoritmu.
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.
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.
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.
Miloš, Marek. "Nástroje pro Big Data Analytics." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199274.
Повний текст джерелаKatzenbach, Alfred, and Holger Frielingsdorf. "Big Data Analytics für die Produktentwicklung." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214517.
Повний текст джерелаSun, Mingyang. "Big data analytics in power systems." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/45061.
Повний текст джерелаBitto, Nicholas. "Adding big data analytics to GCSS-MC." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/43879.
Повний текст джерелаGlobal Combat Support System - Marine Corp is a large logistics system designed to replace numerous legacy systems used by theMarine Corps. While it has been in existence for a while, its intended potential has not been fully realized. Therefore, various teams are working hard to develop the analytics that will benefit the community. With the growth of data, the only way these analytics (in Structured Query Language [SQL]) will run efficiently will be on proprietary hardware from Oracle. This research looks at running the same analytics on commodity hardware using Hadoop Distributed File System and Java Map Reduce. The results show that while it takes longer to program in Java (over SQL), the analytics are just as, or even more powerful ,as SQL, and the potential to save on hardware cost is significant.
TANNEEDI, NAREN NAGA PAVAN PRITHVI. "Customer Churn Prediction Using Big Data Analytics." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13518.
Повний текст джерелаLe, Quoc Do. "Approximate Data Analytics Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234219.
Повний текст джерелаLeis, Machín Angela 1974. "Studying depression through big data analytics on Twitter." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2021. http://hdl.handle.net/10803/671365.
Повний текст джерелаBrydon, Humphrey Charles. "Missing imputation methods explored in big data analytics." University of the Western Cape, 2018. http://hdl.handle.net/11394/6605.
Повний текст джерелаThe aim of this study is to look at the methods and processes involved in imputing missing data and more specifically, complete missing blocks of data. A further aim of this study is to look at the effect that the imputed data has on the accuracy of various predictive models constructed on the imputed data and hence determine if the imputation method involved is suitable. The identification of the missingness mechanism present in the data should be the first process to follow in order to identify a possible imputation method. The identification of a suitable imputation method is easier if the mechanism can be identified as one of the following; missing completely at random (MCAR), missing at random (MAR) or not missing at random (NMAR). Predictive models constructed on the complete imputed data sets are shown to be less accurate for those models constructed on data sets which employed a hot-deck imputation method. The data sets which employed either a single or multiple Monte Carlo Markov Chain (MCMC) or the Fully Conditional Specification (FCS) imputation methods are shown to result in predictive models that are more accurate. The addition of an iterative bagging technique in the modelling procedure is shown to produce highly accurate prediction estimates. The bagging technique is applied to variants of the neural network, a decision tree and a multiple linear regression (MLR) modelling procedure. A stochastic gradient boosted decision tree (SGBT) is also constructed as a comparison to the bagged decision tree. Final models are constructed from 200 iterations of the various modelling procedures using a 60% sampling ratio in the bagging procedure. It is further shown that the addition of the bagging technique in the MLR modelling procedure can produce a MLR model that is more accurate than that of the other more advanced modelling procedures under certain conditions. The evaluation of the predictive models constructed on imputed data is shown to vary based on the type of fit statistic used. It is shown that the average squared error reports little difference in the accuracy levels when compared to the results of the Mean Absolute Prediction Error (MAPE). The MAPE fit statistic is able to magnify the difference in the prediction errors reported. The Normalized Mean Bias Error (NMBE) results show that all predictive models constructed produced estimates that were an over-prediction, although these did vary depending on the data set and modelling procedure used. The Nash Sutcliffe efficiency (NSE) was used as a comparison statistic to compare the accuracy of the predictive models in the context of imputed data. The NSE statistic showed that the estimates of the models constructed on the imputed data sets employing a multiple imputation method were highly accurate. The NSE statistic results reported that the estimates from the predictive models constructed on the hot-deck imputed data were inaccurate and that a mean substitution of the fully observed data would have been a better method of imputation. The conclusion reached in this study shows that the choice of imputation method as well as that of the predictive model is dependent on the data used. Four unique combinations of imputation methods and modelling procedures were concluded for the data considered in this study.
Oikonomidi, Sofia. "Impact of Big Data Analytics in Industry 4.0." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-99443.
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 джерелаHellström, Elin, and My Hemlin. "Det binära guldet : en uppsats om big data och analytics." Thesis, Uppsala universitet, Informationssystem, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-205900.
Повний текст джерелаThe purpose of this study is to investigate the concepts of big data and analytics. The concepts are explored based on scientific theories and interviews with consulting firms. A healthcare organization has also been interviewed to get a richer understanding of how big data and analytics can be used to gain insights and how an organisation can benefit from them. A number of important difficulties and sucess facors connected to the concepts are presented. These difficulties are then linked to a sucess factor that is considered to solve the problem. The most relevant success factors identified are the avaliability of high quality data and knowledge and expertise on how to handle the data. Finally the concepts are clarified and one can see that big data is usually described from the dimensions volume, variety and velocity and analytics is usually described as descriptive and preventive analysis.
Zhang, Liangwei. "Big Data Analytics for eMaintenance : Modeling of high-dimensional data streams." Licentiate thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17012.
Повний текст джерелаGodkänd; 2015; 20150512 (liazha); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Liangwei Zhang Ämne: Drift och underhållsteknik/Operation and Maintenance Engineering Uppsats: Big Data Analytics for eMaintenance Examinator: Professor Uday Kumar Institutionen för samhällsbyggnad och naturresurser Avdelning Drift, underhåll och akustik Luleå tekniska universitet Diskutant: Professor Wolfgang Birk Institutionen för system- och rymdteknik Avdelning Signaler och system Luleå tekniska universitet Tid: Onsdag 10 juni 2015 kl 10.00 Plats: E243, Luleå tekniska universitet
Palummo, Alexandra Lina. "Supporto SQL al sistema Hadoop per big data analytics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016.
Знайти повний текст джерелаMA, YIXIAO. "Big Data Analytics of City Wide Building Energy Declarations." Thesis, KTH, Industriell ekologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-165080.
Повний текст джерелаMoran, Andrew M. Eng Massachusetts Institute of Technology. "Improving big data visual analytics with interactive virtual reality." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105972.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 80-84).
For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.
by Andrew Moran.
M. Eng.
Jun, Sang-Woo. "Scalable multi-access flash store for Big Data analytics." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87947.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 47-49).
For many "Big Data" applications, the limiting factor in performance is often the transportation of large amount of data from hard disks to where it can be processed, i.e. DRAM. In this work we examine an architecture for a scalable distributed flash store which aims to overcome this limitation in two ways. First, the architecture provides a high-performance, high-capacity, scalable random-access storage. It achieves high-throughput by sharing large numbers of flash chips across a low-latency, chip-to-chip backplane network managed by the flash controllers. The additional latency for remote data access via this network is negligible as compared to flash access time. Second, it permits some computation near the data via a FPGA-based programmable flash controller. The controller is located in the datapath between the storage and the host, and provides hardware acceleration for applications without any additional latency. We have constructed a small-scale prototype whose network bandwidth scales directly with the number of nodes, and where average latency for user software to access flash store is less than 70[mu]s, including 3.5[mu]s of network overhead.
by Sang-Woo Jun.
S.M.
Alaka, H. A. "'Big data analytics' for construction firms insolvency prediction models." Thesis, University of the West of England, Bristol, 2017. http://eprints.uwe.ac.uk/30714/.
Повний текст джерелаOlsén, 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.
Повний текст джерелаBig data analytics (BDA) har blivit ett populärt ämne under de senaste åren hos akademiker och utövare. Big data, som är en viktig del av BDA, var först definierad med tre Vs, volym, hastighet och varietet. På senare år har flertalet V framkommit för att bättre uttrycka det nuvarande behovet. Analysdelen i BDA består av olika metoder av analysering av data. Dataanalysering som görs kan ge insikter till organisationer, som i sin tur kan ge organisationer konkurrensfördelar och förbättra deras företag. Genom att definiera de resurser som krävs för att bygga big data analytic capabilities (BDAC), så försökte denna avhandling att visa hur svenska organisationer möjliggör och använder BDA i sina företag. Avhandlingen försökte också härleda om BDA kan leda till prestandaförbättringar och konkurrensfördelar för organisationer. Ett teoretiskt ramverk, baserat på tidigare studier, anpassades och användes för att hjälpa till att svara på avhandlingens syfte. En kvalitativ studie utsågs vara den mest passande ansatsen, tillsammans med semi-strukturerade intervjuer. Tidigare studier inom området visade på att organisationer kanske inte helt är medvetna om hur eller varför BDA möjliggörs eller kan användas. Enligt den nuvarande litteraturen så behöver olika resurser arbeta tillsammans med varandra för att skapa BDAC och möjliggöra att BDA kan utnyttjas till fullo. Flera olika studier diskuterade utmaningar såsom kulturen inom organisationen, kompetens hos anställda och att ledningen behöver stödja BDA initiativ för att lyckas. Fynden från studiens intervjuer indikerade, i ett svenskt sammanhang, att olika resurser såsom data, tekniska färdigheter och datadriven kultur bland annat, används för att möjliggöra BDA. Fortsättningsvis påvisade resultatet att affärsprocessförbättring är en första stapel i användandet av fördelarna från BDA. Anledningen till det är för att det är lättare och säkrare med beräkning av förtjänst och effekt från en sådan investering. Beroende på hur långt en organisation har kommit i deras transformationsprocess kan de också innovera och/eller skapa produkter eller tjänster som möjliggjorts av insikter från BDA.
Mathias, Henry. "Analyzing Small Businesses' Adoption of Big Data Security Analytics." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6614.
Повний текст джерелаVahedian, Khezerlou Amin. "Mining big mobility data for large urban event analytics." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/7039.
Повний текст джерелаHahmann, Martin, Claudio Hartmann, Lars Kegel, Dirk Habich, and Wolfgang Lehner. "Big by blocks: Modular Analytics." De Gruyter, 2016. https://tud.qucosa.de/id/qucosa%3A72848.
Повний текст джерелаCao, Lei. "Outlier Detection In Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/82.
Повний текст джерелаSingh, Shailendra. "Smart Meters Big Data : Behavioral Analytics via Incremental Data Mining and Visualization." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35244.
Повний текст джерелаPlevoets, Christina, and Rodrigo Fernandes. "Exploring the role of Big Data and Analytics : Creating Data-Driven Innovation." Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13463.
Повний текст джерелаStouten, Floris. "Big data analytics attack detection for Critical Information Infrastructure Protection." Thesis, Luleå tekniska universitet, Datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-59562.
Повний текст джерелаJun, Sang-Woo. "Big data analytics made affordable using hardware-accelerated flash storage." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118088.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 175-192).
Vast amount of data is continuously being collected from sources including social networks, web pages, and sensor networks, and their economic value is dependent on our ability to analyze them in a timely and affordable manner. High performance analytics have traditionally required a machine or a cluster of machines with enough DRAM to accommodate the entire working set, due to their need for random accesses. However, datasets of interest are now regularly exceeding terabytes in size, and the cost of purchasing and operating a cluster with hundreds of machines is becoming a significant overhead. Furthermore, the performance of many random-access-intensive applications plummets even when a fraction of data does not fit in memory. On the other hand, such datasets could be stored easily in the flash-based secondary storage of a rack-scale cluster, or even a single machine for a fraction of capital and operating costs. While flash storage has much better performance compared to hard disks, there are many hurdles to overcome in order to reach the performance of DRAM-based clusters. This thesis presents a new system architecture as well as operational methods that enable flash-based systems to achieve performance comparable to much costlier DRAM-based clusters for many important applications. We describe a highly customizable architecture called BlueDBM, which includes flash storage devices augmented with in-storage hardware accelerators, networked using a separate storage-area network. Using a prototype BlueDBM cluster with custom-designed accelerated storage devices, as well as novel accelerator designs and storage management algorithms, we have demonstrated high performance at low cost for applications including graph analytics, sorting, and database operations. We believe this approach to handling Big Data analytics is an attractive solution to the cost-performance issue of Big Data analytics.
by Sang-Woo Jun.
Ph. D.
Bin, Saip Mohamed A. "Big Social Data Analytics: A Model for the Public Sector." Thesis, University of Bradford, 2019. http://hdl.handle.net/10454/18352.
Повний текст джерелаUniversiti Utara Malaysia
Stevens, Melissa Anine. "Creating value from big data and analytics : a leader's perspective." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64819.
Повний текст джерелаMini Dissertation (MBA)--University of Pretoria, 2017.
lt2018
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
Niland, Michael John. "Toward the influence of the organisation on big data analytics." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64902.
Повний текст джерелаMini Dissertation (MBA)--University of Pretoria, 2017.
km2018
Gordon Institute of Business Science (GIBS)
MBA
Unrestricted
Khan, Mukhtaj. "Hadoop performance modeling and job optimization for big data analytics." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/11078.
Повний текст джерелаRashid, 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.
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 джерелаBoquet, Pujadas Guillem. "Contributions to Intelligent Transportation Systems. Big data analytics for reliable and valuable data." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673761.
Повний текст джерелаLa industria del transporte ha entrado en la era del big data. Parte de los datos difundidos por los vehículos y la infraestructura conectados está siendo explotada por Intelligent Transport Systems (ITS), aplicaciones avanzadas en las que las tecnologías de la información y la comunicación se aplican en el campo de la gestión del tráfico del transporte por carretera. En un futuro próximo, es probable que todos los vehículos se comuniquen entre sí y con la infraestructura circundante, por ejemplo, para advertir a otros sobre incidentes de tráfico o las condiciones de la carretera. Sin embargo, los requisitos de conectividad y análisis de datos para los casos de uso previstos están lejos de estar cubiertos. Dedicated Short Range Communication (DSRC) es un estándar basado en la evolución de IEEE 802.11p Wi-Fi, una de las principales tecnologías que permiten el concepto de vehículo conectado. La primera parte de esta tesis aborda la mejora de la comunicación directa entre vehículo e infraestructura mediante IEEE 802.11p en la capa de adquisición de datos ITS. El análisis realizado concluye que la información que recibe la infraestructura difundida a través de los protocolos estandarizados no es lo suficientemente fiable como para permitir aplicaciones de seguridad en los cruces de carreteras. Para solucionar esto, se proponen nuevos criterios orientados a la infraestructura para adecuar los parámetros de comunicación. Además, se diseña un nuevo protocolo alineado con los estándares para intersecciones que aumenta la confiabilidad de la capa de adquisición de datos hasta el punto de permitir la implementación de aplicaciones de seguridad. Debido a que la capa de adquisición de datos produce grandes cantidades de datos, se requiere la agregación y el procesamiento de estos en las capas superiores de aplicación y análisis de datos para permitir casos de uso más avanzados. Por ejemplo, aplicaciones críticas que tienen el potencial impacto de reducir problemas como la seguridad vial, la contaminación, la congestión del tráfico y los costes de transporte. La segunda parte de la tesis propone un modelo generativo basado en deep learning que se puede utilizar de manera no supervisada para resolver múltiples problemas de los ITS. Los datos recopilados por los ITS se explotan y se transforman en un activo valioso para las aplicaciones de seguridad y la toma de decisiones, sin la necesidad de conocimientos adicionales ni de datos etiquetados. El modelo permite comprimir de manera eficiente los datos y pronosticar el tráfico, imputar valores faltantes, seleccionar los mejores datos y modelos para un problema específico y detectar datos de tráfico anómalos al mismo tiempo. La última parte de la tesis está motivada por la creciente preocupación que genera la eficiencia de las soluciones ITS y la gran cantidad de datos que se espera procesar. El algoritmo presentado permite derivar de manera automática y eficiente la mínima arquitectura del modelo que proporciona la máxima compresión de la información y mantiene la máxima información útil sobre los datos de tráfico originales. De esta manera, el rendimiento del sistema de pronóstico de tráfico ITS posterior no se ve afectado negativamente, sino que se beneficia del hecho de que los datos se representan con menos dimensiones, lo cual es de vital importancia en la era del big data. Las bases del algoritmo se toman de conceptos teóricos de la Teoría de la Información aplicados a las redes neuronales, yendo un paso más allá de los métodos actualmente disponibles que se basan en prueba y error.
Transportation industry has entered the era of big data. Part of the data disseminated by connected vehicles and infrastructure is being exploited by Intelligent Transport Systems (ITS), advanced applications in which information and communication technologies are applied in the field of road transport traffic management. In the upcoming future, all road vehicles are likely to communicate with one another and the surrounding infrastructure, for example, to warn others about traffic incidents or poor road conditions. But, the connectivity and data analytics requirements for the envisaged use cases are far from covered. Dedicated Short Range Communication (DSRC) is a higher layer standard based on the evolution of IEEE 802.11p Wi-Fi, one of the main technologies that support the first generation of vehicle-to-everything (V2X) communication. The first part of this dissertation addresses the improvement of IEEE 802.11p direct vehicular-to-infrastructure communication in the ITS data acquisition layer, which suffers from a well-known scalability problem. The analysis carried out concludes that the data dissemination of standardized protocols is not reliable enough to support safety applications that depend on ITS roadside units located in intersection areas. To solve this, novel infrastructure-oriented criteria is proposed to adapt the communication parameters and an intersection assistance protocol is designed in compliance with the standards to increase the reliability of the data acquisition layer up to the point where safety applications can be implemented. As ITS data acquisition layer produces massive amounts of data, it requires data aggregation and processing in the data analytics and application layer to enable more advanced use cases, mission-critical applications that have the potential impact to reduce problems such as road safety, pollution, traffic congestion and transportation costs. The second part of the dissertation proposes a generative deep learning model that can be used in an unsupervised manner to solve multiple ITS challenges. Big data collected by ITS is exploited and transformed to an asset for safety applications and decision-making, without the need for additional knowledge nor labeled data. The model allows to efficiently compress traffic data and forecast, impute missing values, select the best data and models for a specific problem and detect anomalous traffic data at the same time. The last part of the dissertation is motivated by the growing concern generated by the efficiency of ITS solutions and the large amount of data expected to be processed. The presented algorithm allows to automatically and efficiently derive the minimum expression architecture of the model that provides maximal compressed representations that inform about the original traffic data. In this way, the performance of the subsequent ITS traffic forecasting system is not adversely affected, but benefits from data being represented with fewer dimensions, which is vitally important in the age of big data. The basis of the algorithm is taken from theoretical concepts of Information Theory applied to neural networks, going a step beyond the current available methods that are based on trial and error.
Universitat Autònoma de Barcelona. Programa de Doctorat en Enginyeria Electrònica i de Telecomunicació
Gardoni, Pietro. "Big Data Analytics: il valore delle informazioni nella strategia e nell'organizzazione aziendale." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Знайти повний текст джерелаHauptli, Erich Jurg. "ProGENitor : an application to guide your career." Thesis, 2014. http://hdl.handle.net/2152/28120.
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