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Rozprawy doktorskie na temat „Genomics Big Data Engineering”

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Data publikacji: 6 września 2023

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Goldstein, Theodore C. "Tools for extracting actionable medical knowledge from genomic big data." Thesis, University of California, Santa Cruz, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3589324.

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<p> Cancer is an ideal target for personal genomics-based medicine that uses high-throughput genome assays such as DNA sequencing, RNA sequencing, and expression analysis (collectively called <i>omics</i>); however, researchers and physicians are overwhelmed by the quantities of big data from these assays and cannot interpret this information accurately without specialized tools. To address this problem, I have created software methods and tools called <i>OCCAM</i> (OmiC data Cancer Analytic Model) and DIPSC (Differential Pathway Signature Correlation) for automatically extracting knowled

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Miller, Chase Allen. "Towards a Web-Based, Big Data, Genomics Ecosystem." Thesis, Boston College, 2014. http://hdl.handle.net/2345/bc-ir:104052.

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Thesis advisor: Gabor T. Marth<br>Rapid advances in genome sequencing enable a wide range of biological experiments on a scale that was until recently restricted to large genome centers. However, the analysis of the resulting vast genomic datasets is time-consuming, unintuitive and requires considerable computational expertise and costly infrastructure. Collectively, these factors effectively exclude many bench biologists from genome-scale analyses. Web-based visualization and analysis libraries, frameworks, and applications were developed to empower all biological researchers to easily, inter

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

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

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

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

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Schobel, Seth Adam Micah. "The viral genomics revolution| Big data approaches to basic viral research, surveillance, and vaccine development." Thesis, University of Maryland, College Park, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10011480.

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<p> Since the decoding of the first RNA virus in 1976, the field of viral genomics has exploded, first through the use of Sanger sequencing technologies and later with the use next-generation sequencing approaches. With the development of these sequencing technologies, viral genomics has entered an era of big data. New challenges for analyzing these data are now apparent. Here, we describe novel methods to extend the current capabilities of viral comparative genomics. Through the use of antigenic distancing techniques, we have examined the relationship between the antigenic phenotype and the g

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Cheelangi, Madhusudan. "Result Distribution in Big Data Systems." Thesis, University of California, Irvine, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=1539891.

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<p> We are building a Big Data Management System (BDMS) called <b>AsterixDB </b> at UCI. Since AsterixDB is designed to operate on large volumes of data, the results for its queries can be potentially very large, and AsterixDB is also designed to operate under high concurency workloads. As a result, we need a specialized mechanism to manage these large volumes of query results and deliver them to the clients. In this thesis, we present an architecture and an implementation of a new result distribution framework that is capable of handling large volumes of results under high concurency workload

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Laurila, M. (Mikko). "Big data in Finnish financial services." Bachelor's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201711243156.

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This thesis aims to explore the concept of big data, and create understanding of big data maturity in the Finnish financial services industry. The research questions of this thesis are “What kind of big data solutions are being implemented in the Finnish financial services sector?” and “Which factors impede faster implementation of big data solutions in the Finnish financial services sector?”. Big data, being a concept usually linked with huge data sets and economies of scale, is an interesting topic for research in Finland, a market in which the size of data sets is somewhat limited by the si

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

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

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

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

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Adler, Philip David Felix. "Crystalline cheminformatics : big data approaches to crystal engineering." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/410940/.

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Statistical approaches to chemistry, under the umbrella of cheminformatics, are now widespread - in particular as a part of quantitative activity structure relationship and quantitative property structure relationship studies on candidate pharmaceutical studies. Using such approaches on legacy data has widely been termed “taking a big data approach”, and finds ready application in cohort medicinal studies and psychological studies. Crystallography is a field ripe for these approaches, owing in no small part to its history as a field which, by necessity, adopted digital technologies relatively

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Ohlsson, Anna, and Dan Öman. "A guide in the Big Data jungle." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1057.

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This bachelor thesis looks at the functionality of different frameworks for data analysis atlarge scale and the purpose of it is to serve as a guide among available tools. The amount ofdata that is generated every day keep growing and for companies to take advantage of thedata they collect they need to know how to analyze it to gain maximal use out of it. Thechoice of platform for this analysis plays an important role and you need to look in to thefunctionality of the different alternatives that are available. We have created a guide to makethis research easier and less time consuming. To eval

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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.

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Big data is currently a buzzword in both academia and industry, with the term being used todescribe a broad domain of concepts, ranging from extracting data from outside sources, storingand managing it, to processing such data with analytical techniques and tools.This thesis work thus aims to provide a review of current big data analytics concepts in an attemptto highlight big data analytics’ importance to decision making.Due to the rapid increase in interest in big data and its importance to academia, industry, andsociety, solutions to handling data and extracting knowledge from datasets need

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Hellström, Hampus, and Oscar Ohm. "Big Data - Stort intresse, nya möjligheter." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20307.

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Dagens informationssamhälle har bidragit till att människor, maskiner och företag genererar och lagrar stora mängder data. Hanteringen och bearbetningen av de stora datamängderna har fått samlingsnamnet Big Data.De stora datamängderna ökar bland annat möjligheterna att bedriva kunskapsbaserad verksamhetsutveckling. Med traditionella metoder för insamling och analys av data har kunskapsbaserad verksamhetsutveckling tillämpats genom att skicka ut resurskrävande marknadsundersökningar och kartläggningar, ofta genomförda av specialiserade undersökningsföretag. Efterhand som analyser av samhällets

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Huttanus, Herbert M. "Screening and Engineering Phenotypes using Big Data Systems Biology." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/102706.

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Biological systems display remarkable complexity that is not properly accounted for in small, reductionistic models. Increasingly, big data approaches using genomics, proteomics, metabolomics etc. are being applied to predicting and modifying the emergent phenotypes produced by complex biological systems. In this research, several novel tools were developed to assist in the acquisition and analysis of biological big data for a variety of applications. In total, two entirely new tools were created and a third, relatively new method, was evaluated by applying it to questions of clinical importan

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Smith, Derik Lafayette, and Satya Prakash Dhavala. "Using big data for decisions in agricultural supply chain." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81106.

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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 53-54).<br>Agriculture is an industry where historical and current data abound. This paper investigates the numerous data sources available in the agricultural field and analyzes them for usage in supply chain improvement. We identified certain applicable data and investigated methods of using this data to make better supply chain decisions within the agricultural chemical distribution chain. We identified

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Lu, Feng. "Big data scalability for high throughput processing and analysis of vehicle engineering data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-207084.

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"Sympathy for Data" is a platform that is utilized for Big Data automation analytics. It is based on visual interface and workflow configurations. The main purpose of the platform is to reuse parts of code for structured analysis of vehicle engineering data. However, there are some performance issues on a single machine for processing a large amount of data in Sympathy for Data. There are also disk and CPU IO intensive issues when the data is oversized and the platform need fits comfortably in memory. In addition, for data over the TB or PB level, the Sympathy for data needs separate functiona

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Stjerna, Albin. "Medium Data on Big Data Predicting Disk Failures in CERNs NetApp-based Data Storage System." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-337638.

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I describe in this report an experimental system for using classification and regression trees to generate predictions of disk failures in a NetApp-based storage system at the European Organisation for Nuclear Research (CERN) based on a mixture of SMART data, system logs, and low-level system performance dataparticular to NetApp's storage solutions. Additionally, I make an attempt at profiling the system's built-in failure prediction method, and compiling statistics on historical complete-disk failures as well as bad blocks developed. Finally, I experiment with various parameters for producing

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Bao, Shunxing. "Algorithmic Enhancements to Data Colocation Grid Frameworks for Big Data Medical Image Processing." Thesis, Vanderbilt University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13877282.

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<p> Large-scale medical imaging studies to date have predominantly leveraged in-house, laboratory-based or traditional grid computing resources for their computing needs, where the applications often use hierarchical data structures (e.g., Network file system file stores) or databases (e.g., COINS, XNAT) for storage and retrieval. The resulting performance for laboratory-based approaches reveal that performance is impeded by standard network switches since typical processing can saturate network bandwidth during transfer from storage to processing nodes for even moderate-sized studies. On the

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Jiang, Yiming. "Automated Generation of CAD Big Data for Geometric Machine Learning." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1576329384392725.

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Moran, 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.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 80-84).<br>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

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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.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 47-49).<br>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, scalabl

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Hansson, Karakoca Josef. "Big Data Types : Internally Parallel in an Actor Language." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372248.

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Around year 2005 the hardware industry hit a power wall. It was no longer possible to drastically increasing computer performance through decreasing the transistors' size or increasing the clock-speed of the CPU. To ensure future development multi-core processors became the way to go. The Programming Languages Group at Uppsala University is developing a programming language called Encore that is developed to be scalable to future machines with a few hundred or even thousand processor cores. This thesis reports on the design and implementation of Big data types. Big data types are locally distr

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Lindberg, Johan. "Big Data och Hadoop : Nästa generation av lagring." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31079.

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The goal of this report and study is to at a theoretical level determine the possi- bilities for Försäkringskassan IT to change platform for storage of data used in their daily activities. Försäkringskassan collects immense amounts of data ev- eryday containing personal information, lines of programming code, payments and customer service tickets. Today, everything is stored in large relationship databases which leads to problems with scalability and performance. The new platform studied in this report is built on a storage technology named Hadoop. Hadoop is developed to store and process data

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Toole, Jameson Lawrence. "Putting big data in its place : understanding cities and human mobility with new data sources." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98631.

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Thesis: Ph. D., Massachusetts Institute of Technology, Engineering Systems Division, June 2015.<br>Cataloged from PDF version of thesis. "February 2015."<br>Includes bibliographical references (pages 223-241).<br>According the United Nations Population Fund (UNFPA), 2008 marked the first year in which the majority of the planet's population lived in cities. Urbanization, already over 80% in many western regions, is increasing rapidly as migration into cities continue. The density of cities provides residents access to places, people, and goods, but also gives rise to problems related to health

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Bhagattjee, Benoy. "Emergence and taxonomy of big data as a service." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90709.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 82-83).<br>The amount of data that we produce and consume is growing exponentially in the modem world. Increasing use of social media and new innovations such as smartphones generate large amounts of data that can yield invaluable information if properly managed. These large datasets, popularly known as Big Data, are difficult to manage using traditional

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Jun, 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.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 175-192).<br>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

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Battle, Leilani Marie. "Interactive visualization of big data leveraging databases for scalable computation." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84906.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 55-57).<br>Modern database management systems (DBMS) have been designed to efficiently store, manage and perform computations on massive amounts of data. In contrast, many existing visualization systems do not scale seamlessly from small data sets to enormous ones. We have designed a three-tiered visualization system called ScalaR to deal with this issue. ScalaR dynamically performs resolution re

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Wu, Sherwin Zhang. "Sifter : a generalized, efficient, and scalable big data corpus generator." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100684.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 61).<br>Big data has reached the point where the volume, velocity, and variety of data place significant limitations on the computer systems which process and analyze them. Working with very large data sets has becoming increasingly unweildly. Therefore, our goal was to create a system that can support efficient extraction of data subsets to a size that can be manipulated on a single machin

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Eigner, Martin. "Das Industrial Internet – Engineering Prozesse und IT-Lösungen." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214588.

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Das Engineering unterliegt derzeit einem massiven Wandel. Smarte Systeme und Technologien, Cybertronische Produkte, Big Data und Cloud Computing im Kontext des Internet der Dinge und Dienste sowie Industrie 4.0. Der amerikanische Ansatz des „Industrial Internet“ beschreibt diese (R)evolution jedoch weitaus besser als der eingeschränkte und stark deutsch geprägte Begriff Industrie 4.0. Industrial Internet berücksichtigt den gesamten Produktlebenszyklus und adressiert sowohl Konsum- und Investitionsgüter als auch Dienstleistungen. Dieser Beitrag beleuchtet das zukunftsträchtige Trendthema und bi

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Backurs, Arturs. "Below P vs NP : fine-grained hardness for big data problems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120376.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 145-156).<br>The theory of NP-hardness has been remarkably successful in identifying problems that are unlikely to be solvable in polynomial time. However, many other important problems do have polynomial-time algorithms, but

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Bunpuckdee, Bhadin, and Ömer Tekbas. "Ideation with Big Data : A case study of a large mature firm." Thesis, KTH, Maskinkonstruktion (Inst.), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277732.

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Big Data has in recent years gained much attention and interest from organizations. The rise of recent technologies has enabled data to be processed and stored in a simpler manner, thus asking organizations what value Big Data can bring to the organization. However, collecting Big Data does not automatically generate business opportunities; organizations need to understand how to process Big Data and how to implement the insights. To enable this, new competences are needed, and firms need to adapt into more co-innovated constellations. The purpose of this study is to investigate what innovatio

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Landelius, Cecilia. "Data governance in big data : How to improve data quality in a decentralized organization." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301258.

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The use of internet has increased the amount of data available and gathered. Companies are investing in big data analytics to gain insights from this data. However, the value of the analysis and decisions made based on it, is dependent on the quality ofthe underlying data. For this reason, data quality has become a prevalent issue for organizations. Additionally, failures in data quality management are often due to organizational aspects. Due to the growing popularity of decentralized organizational structures, there is a need to understand how a decentralized organization can improve data qua

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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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Akusok, Anton. "Extreme Learning Machines: novel extensions and application to Big Data." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/3036.

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Extreme Learning Machine (ELM) is a recently discovered way of training Single Layer Feed-forward Neural Networks with an explicitly given solution, which exists because the input weights and biases are generated randomly and never change. The method in general achieves performance comparable to Error Back-Propagation, but the training time is up to 5 orders of magnitude smaller. Despite a random initialization, the regularization procedures explained in the thesis ensure consistently good results. While the general methodology of ELMs is well developed, the sheer speed of the method enables i

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Dawany, Noor Tozeren Aydin. "Large-scale integration of microarray data : investigating the pathologies of cancer and infectious diseases /." Philadelphia, Pa. : Drexel University, 2010. http://hdl.handle.net/1860/3251.

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Kalila, Adham. "Big data fusion to estimate driving adoption behavior and urban fuel consumption." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119335.

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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 63-68).<br>Data from mobile phones is constantly increasing in accuracy, quantity, and ubiquity. Methods that utilize such data in the field of transportation demand forecasting have been proposed and represent a welcome addition. We propose a framework that uses the resulting travel demand and computes fuel consumption. The model is calibrated for application on any range of car fu

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Abounia, 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.

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Khalilikhah, Majid. "Traffic Sign Management: Data Integration and Analysis Methods for Mobile LiDAR and Digital Photolog Big Data." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/4744.

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This study links traffic sign visibility and legibility to quantify the effects of damage or deterioration on sign retroreflective performance. In addition, this study proposes GIS-based data integration strategies to obtain and extract climate, location, and emission data for in-service traffic signs. The proposed data integration strategy can also be used to assess all transportation infrastructures’ physical condition. Additionally, non-parametric machine learning methods are applied to analyze the combined GIS, Mobile LiDAR imaging, and digital photolog big data. The results are presented

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Purcaro, Michael J. "Analysis, Visualization, and Machine Learning of Epigenomic Data." eScholarship@UMMS, 2017. https://escholarship.umassmed.edu/gsbs_diss/938.

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The goal of the Encyclopedia of DNA Elements (ENCODE) project has been to characterize all the functional elements of the human genome. These elements include expressed transcripts and genomic regions bound by transcription factors (TFs), occupied by nucleosomes, occupied by nucleosomes with modified histones, or hypersensitive to DNase I cleavage, etc. Chromatin Immunoprecipitation (ChIP-seq) is an experimental technique for detecting TF binding in living cells, and the genomic regions bound by TFs are called ChIP-seq peaks. ENCODE has performed and compiled results from tens of thousands of

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Li, Zhen. "CloudVista: a Framework for Interactive Visual Cluster Exploration of Big Data in the Cloud." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1348204863.

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Pergert, Anton, and William George. "Teoretisk undersökning om relationen mellan Big Data och ekologisk hållbarhet i tillverkande industri." Thesis, KTH, Maskinkonstruktion (Inst.), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299636.

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Industriella revolutionen hade sin begynnelse under mitten av 1700-talet. Idag befinner vi oss i början av den fjärde revolutionen, även känd som Industri 4.0 där smarta teknologier integreras i fabriker. Ett resultat av detta är insamlandet och hanteringen av stora mängder data, vilket introducerat Big Data i den tillverkande industrin. Samtidigt växer fokuset på ekologisk hållbarhet på grund av den ökade miljöförstöringen och utarmningen av naturliga resurser. Därför är en viktig aspekt av Industri 4.0 at implementera smarta teknologier som gör fabriker mer ekologiskt hållbara. Denna studie

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Kumlin, Jesper. "True operation simulation for urban rail : Energy efficiency from access to Big data." Thesis, Mälardalens högskola, Industriell ekonomi och organisation, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44264.

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Obeso, Duque Aleksandra. "Performance Prediction for Enabling Intelligent Resource Management on Big Data Processing Workflows." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372178.

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Mobile cloud computing offers an augmented infrastructure that allows resource-constrained devices to use remote computational resources as an enabler for highly intensive computation, thus improving end users experience. Being able to efficiently manage cloud elasticity represents a big challenge for dynamic resource scaling on-demand. In this sense, the development of intelligent tools that could ease the understanding of the behavior of a highly dynamic system and to detect resource bottlenecks given certain service level constrains represents an interesting case of study. In this project,

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Koseler, Kaan Tamer. "Realization of Model-Driven Engineering for Big Data: A Baseball Analytics Use Case." Miami University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=miami1524832924255132.

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

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

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Taratoris, Evangelos. "A single-pass grid-based algorithm for clustering big data on spatial databases." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113168.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 79-80).<br>The problem of clustering multi-dimensional data has been well researched in the scientific community. It is a problem with wide scope and applications. With the rapid growth of very large databases, traditional clustering algorithms become inefficient due to insufficient memory capacity. Grid-based algorithms try to solve this problem by dividing the space into cells and then pe

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Zhang, 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.

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Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems a

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Newth, Oliver Edward. "Predicting extreme events : the role of big data in quantifying risk in structural development." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/90028.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-73).<br>Engineers are well-placed when calculating the required resistance for natural and non-natural hazards. However, there are two main problems with the current approach. First, while hazards are one of the primary causes of catastrophic damage and the design against risk contributes vastly to the cost in design and construction, it is only considered late in the development process. Secon

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Guzun, Gheorghi. "Distributed indexing and scalable query processing for interactive big data explorations." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2087.

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The past few years have brought a major surge in the volumes of collected data. More and more enterprises and research institutions find tremendous value in data analysis and exploration. Big Data analytics is used for improving customer experience, perform complex weather data integration and model prediction, as well as personalized medicine and many other services. Advances in technology, along with high interest in big data, can only increase the demand on data collection and mining in the years to come. As a result, and in order

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