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Dissertations / Theses on the topic 'Big data and data mining'

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Published: 4 June 2021
Last updated: 18 February 2022

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1

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

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This project is an extension of i2MapReduce: Incremental MapReduce for Mining Evolving Big Data . i2MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such as PageRank, Fuzzy-C-Means(FCM), Generalized Iterated Matrix-Vector Multiplication(GIM-V), Single Source Shortest Path(SSSP). The main purpose of this project is to reduce input/output overhead, to avoid incurring the cost of re-computation and avoid stale data mining results. Finally, the performance of i2MapReduce is anal
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Al-Hashemi, Idrees Yousef. "Applying data mining techniques over big data." Thesis, Boston University, 2013. https://hdl.handle.net/2144/21119.

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Thesis (M.S.C.S.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>The rapid development of information technology in recent decades means that data appear in a wide variety of formats — sensor data, tweets, photographs, raw data, and unstructured data. Statistics show that there were 800,000
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Bernsdorf, Bodo, and Julian Bruns. "Big Data und Data-Mining im Umfeld städtischer Nutzungskartierung." Rhombos-Verlag, 2016. https://slub.qucosa.de/id/qucosa%3A16835.

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Es ist festzustellen, dass die städtische Nutzungskartierung auf immer mehr Datenquellen zurückgreifen kann. Insbesondere handelt es sich um hochauflösende (Geo-)Daten von Fernerkundungsplattformen wie Satelliten aus dem Copernicus-Programm. Aber auch sogenannte Volunteer Geographic Information (VGI) spielen eine zunehmende Rolle. Speziell entwickelte Anwendungsprogramme, sogenannte „Apps“, kommen zum Sammeln solcher Rauminformationen in Frage. Und letztlich kommen Daten aus sozialen Netzwerken zum Tragen. Dieser Beitrag beschäftigt sich mit der Anwendung von Big Data im geo-temporalen Umfeld:
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Liu, Lian. "PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.

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Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbati
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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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Singh, 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.

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The big data framework applied to smart meters offers an exception platform for data-driven forecasting and decision making to achieve sustainable energy efficiency. Buying-in consumer confidence through respecting occupants' energy consumption behavior and preferences towards improved participation in various energy programs is imperative but difficult to obtain. The key elements for understanding and predicting household energy consumption are activities occupants perform, appliances and the times that appliances are used, and inter-appliance dependencies. This information can be extracted f
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Melgueira, Pedro Miguel Lúcio. "Educational data mining applied to Moodle data from the University of Évora." Master's thesis, Universidade de Évora, 2017. http://hdl.handle.net/10174/21346.

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E-Learning tem vindo a ganhar popularidade como forma de transmissão de conhecimentos a nível educacional graças aos avanços nas tecnologias, como por exemplo, a Internet. Instituições como universidades e empresas têm vindo a usar E-Learning para a transmissão de conteúdos educacionais para locais remotos estendendo o seu alcance a estudantes e colaboradores que estão fisicamente distantes. Sistemas chamados “Learning Management Systems”, como o Moodle, existem para organizar E-Learning. Eles oferecem plataformas online onde professores e educadores podem publicar conteúdo, organizar activida
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8

Carvalho, Danilo Codeco. "Obtenção de padrões sequenciais em data streams atendendo requisitos do Big Data." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8280.

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Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-10-20T18:13:56Z No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:36Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:42Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Made available
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Vahedian, Khezerlou Amin. "Mining big mobility data for large urban event analytics." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/7039.

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This thesis seeks to formulate concepts and develop methods that facilitate the mining of urban big mobility data. Specifically, the aim of the formulations and developed methods is to identify and predict certain events that occur as a result of urban mobility. This thesis, studies unexpected gathering and dispersal events. A Gathering event is the process of an unusually large number of moving objects (e.g. taxi) arriving at the same area within a short period of time. It is important for city management to identify emerging gathering events which might cause public safety or sustainability
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10

Jiang, Fan. "Efficient frequent pattern mining from big data and its applications." Springer, 2014. http://hdl.handle.net/1993/32083.

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Frequent pattern mining is an important research areas in data mining. Since its introduction, it has drawn attention of many researchers. Consequently, many algorithms have been proposed. Popular algorithms include level-wise Apriori based algorithms, tree based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and beneficial due to some nice properties, they also suffer from some drawbacks such as multiple database scans, recursive tree constructions, or multiple hyperlink adjustments. In the current era of big data, high volumes of a wide varie
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Sohangir, Soroosh. "MACHINE LEARNING ALGORITHM PERFORMANCE OPTIMIZATION: SOLVING ISSUES OF BIG DATA ANALYSIS." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/dissertations/1111.

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Because of high complexity of time and space, generating machine learning models for big data is difficult. This research is introducing a novel approach to optimize the performance of learning algorithms with a particular focus on big data manipulation. To implement this method a machine learning platform using eighteen machine learning algorithms is implemented. This platform is tested using four different use cases and result is illustrated and analyzed.
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Nilsson, Per. "Användningsområden för Big data inom analytisk CRM." Thesis, Mittuniversitetet, Avdelningen för arkiv- och datavetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-23305.

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Customer Relationship Management (CRM) är ett ofta använt konceptför verksamheter att hantera sina kundkontakter. En viktig del av CRMär användningen av tekniska lösningar för att lagra och analysera informationom kunder, till exempel genom data mining för att upptäckamönster hos kunders beteende. Idag produceras allt större mängderdata genom människors användning av informations- och kommunikationsteknik.Traditionell teknik klarar ej av att hantera den variation ochmängd data som existerar, vilket lett till utvecklingen av nya tekniskalösningar för dessa uppgifter. Begreppet Big data brukar a
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Silva, Jesús, Palma Hugo Hernández, Núẽz William Niebles, David Ovallos-Gazabon, and Noel Varela. "Parallel Algorithm for Reduction of Data Processing Time in Big Data." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652134.

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Technological advances have allowed to collect and store large volumes of data over the years. Besides, it is significant that today's applications have high performance and can analyze these large datasets effectively. Today, it remains a challenge for data mining to make its algorithms and applications equally efficient in the need of increasing data size and dimensionality [1]. To achieve this goal, many applications rely on parallelism, because it is an area that allows the reduction of cost depending on the execution time of the algorithms because it takes advantage of the characteristics
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14

Raza, Atif [Verfasser]. "Metaheuristics for Pattern Mining in Big Sequence Data / Atif Raza." Mainz : Universitätsbibliothek der Johannes Gutenberg-Universität Mainz, 2021. http://d-nb.info/1231992875/34.

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15

Nhlabano, Valentine Velaphi. "Fast Data Analysis Methods For Social Media Data." Diss., University of Pretoria, 2018. http://hdl.handle.net/2263/72546.

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The advent of Web 2.0 technologies which supports the creation and publishing of various social media content in a collaborative and participatory way by all users in the form of user generated content and social networks has led to the creation of vast amounts of structured, semi-structured and unstructured data. The sudden rise of social media has led to their wide adoption by organisations of various sizes worldwide in order to take advantage of this new way of communication and engaging with their stakeholders in ways that was unimaginable before. Data generated from social media is highly
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Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Bi
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Naldini, Federico. "Clustering di traiettorie in ambito big data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20480/.

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Uno dei trend più interessanti del momento è l'analisi e mining dei dati di traiettoria. Questa categoria di dati si compone principalmente delle tracce di movimento generate dalle più svariate categorie di dispositivi. Una traiettoria può essere interpretata come il cambiamento della posizione di un utente o oggetto nello spazio rispetto al tempo. Nell'ambito dell'analisi di traiettorie, le tecniche di clustering possono essere impiegate con diversi obbiettivi, come ad esempio la ricerca delle strade più frequentate o la profilazione degli utenti. Altrettante potenzialità sono racchiuse nella
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18

Oriani, Mattia. "Clustering di traiettorie su piattaforma big data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18107/.

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Nell’ambito di analisi dei dati di traiettoria, le tecniche di clustering sono utilizzate con diversi obiettivi, dalla scoperta di strade ad alta percorrenza, predizione di destinazioni, fino allo studio del movimento. A seconda della tipologia, gli algoritmi di clustering si suddividono in: algoritmi basati sulla densità, algoritmi basati sul flusso, algoritmi basati sulla distanza. Tra gli algoritmi di clustering basati su flussi di traiettorie, NEAT è tra i più recenti ed è sequenziale, tiene conto dei vincoli della rete stradale, della prossimità fra le strade e del flusso di movimento
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Djuric, Nemanja. "Big Data Algorithms for Visualization and Supervised Learning." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/239445.

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Computer and Information Science<br>Ph.D.<br>Explosive growth in data size, data complexity, and data rates, triggered by emergence of high-throughput technologies such as remote sensing, crowd-sourcing, social networks, or computational advertising, in recent years has led to an increasing availability of data sets of unprecedented scales, with billions of high-dimensional data examples stored on hundreds of terabytes of memory. In order to make use of this large-scale data and extract useful knowledge, researchers in machine learning and data mining communities are faced with numerous challe
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20

Liu, Fang. "Mining Security Risks from Massive Datasets." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78684.

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Cyber security risk has been a problem ever since the appearance of telecommunication and electronic computers. In the recent 30 years, researchers have developed various tools to protect the confidentiality, integrity, and availability of data and programs. However, new challenges are emerging as the amount of data grows rapidly in the big data era. On one hand, attacks are becoming stealthier by concealing their behaviors in massive datasets. One the other hand, it is becoming more and more difficult for existing tools to handle massive datasets with various data types. This thesis presen
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Fallahi, Faraz. "MACHINE LEARNING ON BIG DATA FOR STOCK MARKET PREDICTION." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2178.

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In recent decades, the rapid development of information technology in the big data field has introduced new opportunities to explore a large amount of data available online. The Global Database of Events, Location (Language), and Tone (GDELT) is the largest, most comprehensive, and highest resolution open source database of human society that includes more than 440 million entries capturing information about events that have been covered by local, national, and international news sources since 1979 in over 100 languages. GDELT constructs a catalog of human societal-scale behavior and beliefs a
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Firsov, Vitaly. "Big Data a jejích potenciál pro bankovní sektor." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-165114.

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In this thesis, I want to explore present (y. 2012/2013) modern trends in Business Intelligence and focus specifically on the rapidly evolving and, in my (and not only) opinion, a very perspective area of analysis and use of Big Data in large enterprises. The first, introductory part contains general information and the formal conditions as aims of the work, on whom the work is oriented and where it could be used. Then there are described inputs and outputs, structure, methods to achieve the objectives, potential benefits and limitations in this part. Because at the same time I work as a data
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Leis, 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.

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Mental disorders have become a major concern in public health, since they are one of the main causes of the overall disease burden worldwide. Depressive disorders are the most common mental illnesses, and they constitute the leading cause of disability worldwide. Language is one of the main tools on which mental health professionals base their understanding of human beings and their feelings, as it provides essential information for diagnosing and monitoring patients suffering from mental disorders. In parallel, social media platforms such as Twitter, allow us to observe the activity, though
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Gleue, Christoph [Verfasser]. "Data Mining und Big Data Analytics : semantische Suche, Prognose und Entscheidungsunterstützung mit Künstlichen Neuronalen Netzen / Christoph Gleue." Hannover : Gottfried Wilhelm Leibniz Universität Hannover, 2019. http://d-nb.info/1188406469/34.

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Niggemann, Oliver. "Visual data mining of graph based data." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962400505.

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Price, Lauren Emilie. "Mental Health Readmissions Among Veterans: An Exploratory Endeavor Using Data Mining." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/594949.

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The purpose of this research is to inform the understanding of mental health readmissions by identifying associations between individual and environmental attributes and readmissions, with consideration of the impact of time-to-readmission within the Veterans Health Administration (VHA). Mental illness affects one in five adults in the United States (US). Mental health disorders are among the highest all-cause readmission diagnoses. The VHA is one of the largest national service providers of specialty mental health care. VHA's clinical practices and patient outcomes can be traced to US policy,
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Palmqvist, Simon. "Validating the Quality of a Big Data Java Corpus." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75410.

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Recent research within the field of Software Engineering have used GitHub, the largest hub for open source projects with almost 20 million users and 57 million repositories, to mine large amounts of source code to get more trustworthy results when developing machine and deep learning models. Mining GitHub comes with many challenges since the dataset is large and the data does not only contain quality software projects. In this project, we try to mine projects from GitHub based on earlier research by others and try to validate the quality by comparing the projects with a small subset of quality
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丁嘉慧 and Ka-wai Ting. "Time sequences: data mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226760.

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Mgudlwa, Sibulela. "A big data analytics framework to improve healthcare service delivery in South Africa." Thesis, Cape Peninsula University of Technology, 2018. http://hdl.handle.net/20.500.11838/2877.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2018.<br>Healthcare facilities in South Africa accumulate big data, daily. However, this data is not being utilised to its full potential. The healthcare sector still uses traditional methods to store, process, and analyse data. Currently, there are no big data analytics tools being used in the South African healthcare environment. This study was conducted to establish what factors hinder the effective use of big data in the South African healthcare environment. To fulfil the objectives of this research, qualita
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Virkkala, Linda, and Johanna Haglund. "Modelling of patterns between operational data, diagnostic trouble codes and workshop history using big data and machine learning." Thesis, Uppsala universitet, Datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279823.

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The work presented in this thesis is part of a large research and development project on condition-based maintenance for heavy trucks and buses at Scania. The aim of this thesis was to be able to predict the status of a component (the starter motor) using data mining methods and to create models that can predict the failure of that component. Based on workshop history data, error codes and operational data, three sets of classification models were built and evaluated. The first model aims to find patterns in a set of error codes, to see which codes are related to a starter motor failure. The s
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Addimando, Alessio. "Progettazione di un intrusion detection system su piattaforma big data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16755/.

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Negli ultimi anni, nel panorama digitale, è stato rilevato un ingente aumento del numero di dispositivi e utenti con accesso ad Internet. Proporzionalmente a questi fattori ogni giorno vengono generati continuamente, e in qualsiasi contesto, grandi quantità di dati difficili da gestire. Questo ha fatto emergere la necessità di riorganizzare gli asset aziendali per far fronte ad un calibro di informazione maggiore e per far in modo che la gestione stessa ne estragga valore concreto per la realtà decisionale. L'insieme di queste motivazioni da vita al fenomeno dei Big Data. Affiancato a ques
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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 &amp; 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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Savalli, Antonino. "Tecniche analitiche per “Open Data”." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17476/.

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L’ultimo decennio ha reso estremamente popolare il concetto di Open Government, un modello di amministrazione aperto che fonda le sue basi sui principi di trasparenza, partecipazione e collaborazione. Nel 2011, nasce il progetto Dati.gov.it, un portale che ha il ruolo di “catalogo nazionale dei metadati relativi ai dati rilasciati in formato aperto dalle pubbliche amministrazioni italiane”. L'obiettivo della tesi è fornire un efficace strumento per ricercare, usare e confrontare le informazioni presenti sul portale Dati.gov.it, individuando tra i dataset similarità che possano risolvere e/o l
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Michels, Kurt Andrew. "New Statistical Methods and Computational Tools for Mining Big Data, with Applications in Plant Sciences." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/613247.

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The purpose of this dissertation is to develop new statistical tools for mining big data in plant sciences. In particular, the dissertation consists of four inter-related projects to address various methodological and computational challenges in phylogenetic methods. Project 1 aims to systematically test different optimization tools and provide useful strategies to improve optimization in practice. Project 2 develops a new R package rPlant, which provides a friendly and convenient toolbox for users of iPlant. Project 3 presents a fast and effective group-screening method to identify important
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Sodhi, Bir Apaar Singh. "DATA MINING: TRACKING SUSPICIOUS LOGGING ACTIVITY USING HADOOP." CSUSB ScholarWorks, 2016. https://scholarworks.lib.csusb.edu/etd/271.

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In this modern rather interconnected era, an organization’s top priority is to protect itself from major security breaches occurring frequently within a communicational environment. But, it seems, as if they quite fail in doing so. Every week there are new headlines relating to information being forged, funds being stolen and corrupt usage of credit card and so on. Personal computers are turned into “zombie machines” by hackers to steal confidential and financial information from sources without disclosing hacker’s true identity. These identity thieves rob private data and ruin the very purpos
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Jiang, Shan Ph D. Massachusetts Institute of Technology. "Deciphering human activities in complex urban systems : mining big data for sustainable urban future." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101369.

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Thesis: Ph. D. in Urban and Regional Planning, Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 187-200).<br>"Big Data" is in vogue, and the explosion of urban sensors, mobile phone traces, and other windows onto urban activities has generated much hype about the advent of a new 'urban science.' However, translating such Big Data into a planning-relevant understanding of activity patterns and travel behavior presents a number of obstacles. This dissertation examines some
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Tong, Suk-man Ivy, and 湯淑敏. "Techniques in data stream mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34737376.

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Blázquez, Soriano María Desamparados. "Design and Evaluation of Web-Based Economic Indicators: A Big Data Analysis Approach." Doctoral thesis, Universitat Politècnica de València, 2020. http://hdl.handle.net/10251/116836.

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[ES] En la Era Digital, el creciente uso de Internet y de dispositivos digitales está transformando completamente la forma de interactuar en el contexto económico y social. Miles de personas, empresas y organismos públicos utilizan Internet en sus actividades diarias, generando de este modo una enorme cantidad de datos actualizados ("Big Data") accesibles principalmente a través de la World Wide Web (WWW), que se ha convertido en el mayor repositorio de información del mundo. Estas huellas digitales se pueden rastrear y, si se procesan y analizan de manera apropiada, podrían ayudar a monitoriz
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Arifuzzaman, S. M. "Parallel Mining and Analysis of Triangles and Communities in Big Networks." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72281.

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A network (graph) is a powerful abstraction for interactions among entities in a system. Examples include various social, biological, collaboration, citation, and co-purchase networks. Real-world networks are often characterized by an abundance of triangles and the existence of well-structured communities. Thus, counting triangles and detecting communities in networks have become important algorithmic problems in network mining and analysis. In the era of big data, the network data emerged from numerous scientific disciplines are very large. Online social networks such as Twitter and Facebook
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Mendes, Renê de Ávila. "Aplicação da arquitetura lambda na construção de um ambiente big data educacional para análise de dados." Universidade Presbiteriana Mackenzie, 2017. http://tede.mackenzie.br/jspui/handle/tede/3441.

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41

Medlej, Maguy. "Big data management for periodic wireless sensor networks." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA2029/document.

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Les recherches présentées dans ce mémoire s’inscrivent dans le cadre des réseaux decapteurs périodiques. Elles portent sur l’étude et la mise en oeuvre d’algorithmes et de protocolesdistribués dédiés à la gestion de données volumineuses, en particulier : la collecte, l’agrégation etla fouille de données. L’approche de la collecte de données permet à chaque noeud d’adapter sontaux d’échantillonnage à l’évolution dynamique de l’environnement. Par ce modèle le suréchantillonnageest réduit et par conséquent la quantité d’énergie consommée. Elle est basée surl’étude de la dépendance de la variance
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42

Borgelt, Christian. "Data mining with graphical models." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962912107.

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43

Weber, Irene. "Suchraumbeschränkung für relationales Data Mining." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11380447.

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44

Kimmerle, Joachim. "Data Mining im Pharma-Großhandel." [S.l. : s.n.], 2000. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937692.

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45

Song, Ge. "Méthodes parallèles pour le traitement des flux de données continus." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC059/document.

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Nous vivons dans un monde où une grande quantité de données est généré en continu. Par exemple, quand on fait une recherche sur Google, quand on achète quelque chose sur Amazon, quand on clique en ‘Aimer’ sur Facebook, quand on upload une image sur Instagram, et quand un capteur est activé, etc., de nouvelles données vont être généré. Les données sont différentes d’une simple information numérique, mais viennent dans de nombreux format. Cependant, les données prisent isolément n’ont aucun sens. Mais quand ces données sont reliées ensemble on peut en extraire de nouvelles informations. De plus,
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46

Asenjo, Juan C. "Data Masking, Encryption, and their Effect on Classification Performance: Trade-offs Between Data Security and Utility." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1010.

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As data mining increasingly shapes organizational decision-making, the quality of its results must be questioned to ensure trust in the technology. Inaccuracies can mislead decision-makers and cause costly mistakes. With more data collected for analytical purposes, privacy is also a major concern. Data security policies and regulations are increasingly put in place to manage risks, but these policies and regulations often employ technologies that substitute and/or suppress sensitive details contained in the data sets being mined. Data masking and substitution and/or data encryption and suppres
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Paffumi, Elena, Gennaro Michele De, and Giorgio Martini. "European-wide study on big data for supporting road transport policy." Elsevier, 2018. https://publish.fid-move.qucosa.de/id/qucosa%3A73230.

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This paper presents the latest achievements of TEMA (Transport Technology and Mobility Assessment) platform, designed to harness the potential of big data to support road transport policies in Europe. The platform relies on datasets of real world driving and mobility patterns collected by means of navigation systems and it is developed by the EC Joint Research Centre since 2012. Previous studies have demonstrated the potential of the platform in assessing real world emissions from conventional fuel vehicles and exploring the impact of the deployment of electrified vehicles in terms of usabilit
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Besson, Henrik. "Konsulters beskrivning av Big Data och dess koppling till Business Intelligence." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-22747.

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De allra flesta av oss kommer ständigt i kontakt med olika dataflöden vilket har blivit en helt naturlig del av vårt nutida informationssamhälle. Dagens företag agerar i en ständigt föränderlig omvärld, och hantering av data och information har blivit en allt viktigare konkurrensfaktor. Detta i takt med att den totala datamängden i den digitala världen har ökat kraftigt de senaste åren. En benämning för gigantiska datamängder är Big Data, som har blivit ett populärt begrepp inom IT-branschen. Big Data kommer med helt nya analysmöjligheter, men det har visat sig att många företag är oroliga för
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Bockermann, Christian [Verfasser], Katharina [Akademischer Betreuer] Morik, and Albert [Gutachter] Bifet. "Mining big data streams for multiple concepts / Christian Bockermann. Betreuer: Katharina Morik. Gutachter: Albert Bifet." Dortmund : Universitätsbibliothek Dortmund, 2015. http://d-nb.info/1111103259/34.

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Ao, Sio-iong, and 區小勇. "Data mining algorithms for genomic analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38319822.

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