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Dissertations / Theses on the topic 'Streaming Data Analysis'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

Anagnostopoulos, Christoforos. "A Statistical Framework for Streaming Data Analysis." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520838.

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2

Patni, Harshal Kamlesh. "Real Time Semantic Analysis of Streaming Sensor Data." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1324181415.

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3

Fairbanks, James Paul. "Graph analysis combining numerical, statistical, and streaming techniques." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54972.

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Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra. This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each co

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4

Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.

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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the appli

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Giannini, Andrea. "Social Network Analysis: Architettura Streaming Big Data di Raccolta e Analisi Dati da Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25378/.

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Negli ultimi anni i social media, come ad esempio Facebook, Twitter, WhatsApp, YouTube, si sono diffusi a macchia d'olio. Ormai quasi tutti accedono giornalmente su almeno uno di questi per informarsi, esprimere opinioni e interagire con altri utenti. Per questa ragione sono diventati fondamentali per i reparti marketing delle aziende essendo non solo un ottimo canale di comunicazione, ma anche una fonte di informazioni sui clienti e potenziali tali. La tesi si focalizza proprio su quest'ultimo aspetto. Il progetto Social Network Analysis (SNA) vuole essere infatti uno strumento attravers

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Kühn, Eileen [Verfasser], and A. [Akademischer Betreuer] Streit. "Online Analysis of Dynamic Streaming Data / Eileen Kühn ; Betreuer: A. Streit." Karlsruhe : KIT-Bibliothek, 2018. http://d-nb.info/1161008721/34.

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7

Moitra, Anindya. "Computation and Application of Persistent Homology on Streaming Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613686214764863.

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Zubeir, Abdulghani Ismail. "OAP: An efficient online principal component analysis algorithm for streaming EEG data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392403.

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Data processing on streaming data poses computational as well as statistical challenges. Streaming data requires that data processing algorithms are able to process a new data point within micro-seconds. This is especially challenging on dimension reduction, where traditional methods as Principal Component Analysis (PCA) require eigenvectors decomposition of a matrix based on the complete dataset. So a proper online version of PCA should avoid this computational involved step in favor for a more efficient update rule. This is implemented by an algorithm named Online Angle Preservation (OAP), w

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9

Vigraham, Sushrutha. "Design and Analysis of a Real-time Data Monitoring Prototype for the LWA Radio Telescope." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31306.

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Increasing computing power has been helping researchers understand many complex scientific problems. Scientific computing helps to model and visualize complex processes such as molecular modelling, medical imaging, astrophysics and space exploration by processing large set of data streams collected through sensors or cameras. This produces a massive amount of data which consume a large amount of processing and storage resources. Monitoring the data streams and filtering unwanted information will enable efficient use of the available resources. This thesis proposes a data-centric system that ca

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Landford, Jordan. "Event Detection Using Correlation within Arrays of Streaming PMU Data." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3031.

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This thesis provides a synchrophasor data analysis methodology that leverages both statistical correlation techniques and a statistical distribution in order to identify data inconsistencies, as well as power system contingencies. This research utilizes archived Phasor Measurement Unit (PMU) data obtained from the Bonneville Power Administration in order to show that this methodology is not only feasible, but extremely useful for power systems monitoring, decision support, and planning purposes. By analyzing positive sequence voltage angles between a pair of PMUs at two different substation lo

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11

Akhmedov, Iliiazbek. "Parallelization of Push-based System for Molecular Simulation Data Analysis with GPU." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6448.

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Modern simulation systems generate big amount of data, which consequently has to be analyzed in a timely fashion. Traditional database management systems follow principle of pulling the needed data, processing it, and then returning the results. This approach is then optimized by means of caching, storing in different structures, or doing some sacrifices on precision of the results to make it faster. When it comes to the point of doing various queries that require analysis of the whole data, this design has the following disadvantages: considerable overhead on traditional disk random I/O frame

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12

Zhou, Pu. "A dynamic approximate representation scheme for streaming time series." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/6766.

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The huge volume of time series data generated in many applications poses new challenges in the techniques of data storage, transmission, and computation. Further more, when the time series are in the form of streaming data, new problems emerge and new techniques are required because of the streaming characteristics, e.g. high volume, high speed and continuous flowing. Approximate representation is one of the most efficient and effective solutions to address the large-volume-high-speed problem. In this thesis, we propose a dynamic representation scheme for streaming time series. Existing method

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13

Grupchev, Vladimir. "Improvements on Scientific System Analysis." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5851.

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Thanks to the advancement of the modern computer simulation systems, many scientific applications generate, and require manipulation of large volumes of data. Scientific exploration substantially relies on effective and accurate data analysis. The shear size of the generated data, however, imposes big challenges in the process of analyzing the system. In this dissertation we propose novel techniques as well as using some known designs in a novel way in order to improve scientific data analysis. We develop an efficient method to compute an analytical query called spatial distance histogram (SDH

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14

Rivetti, di Val Cervo Nicolo. "Efficient Stream Analysis and its Application to Big Data Processing." Thesis, Nantes, 2016. http://www.theses.fr/2016NANT4046/document.

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L’analyse de flux de données est utilisée dans beaucoup de contexte où la masse des données et/ou le débit auquel elles sont générées, excluent d’autres approches (par exemple le traitement par lots). Le modèle flux fourni des solutions aléatoires et/ou fondées sur des approximations pour calculer des fonctions d’intérêt sur des flux (repartis) de n-uplets, en considérant le pire cas, et en essayant de minimiser l’utilisation des ressources. En particulier, nous nous intéressons à deux problèmes classiques : l’estimation de fréquence et les poids lourds. Un champ d’application moins courant es

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15

Ahmed, Kachkach. "Analyzing user behavior and sentiment in music streaming services." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186527.

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These last years, streaming services (for music, podcasts, TV shows and movies) have been under the spotlight by disrupting traditional media consumption platforms. If the technical implications of streaming huge amounts of data are well researched, much remains to be done to analyze the wealth of data collected by these services and exploit it to its full potential in order to improve them. Using raw data about users’ interactions with the music streaming service Spotify, this thesis focuses on three main concepts: streaming context, user attention and the sequential analysis of user actions.

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Hilley, David B. "Temporal streams programming abstractions for distributed live stream analysis applications /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31695.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.<br>Committee Chair: Ramachandran, Umakishore; Committee Member: Clark, Nathan; Committee Member: Haskin, Roger; Committee Member: Pu, Calton; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Gratorp, Christina. "Bitrate smooting: a study on traffic shaping and -analysis in data networks." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10136.

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<p>Examensarbetet bakom denna rapport utgör en undersökande studie om hur transmission av mediadata i nätverk kan göras effektivare. Det kan åstadkommas genom att viss tilläggsinformation avsedd för att jämna ut datatakten adderas i det realtidsprotokoll, Real Time Protocol, som används för strömmande media. Genom att försöka skicka lika mycket data under alla konsekutiva tidsintervall i sessionen kommer datatakten vid en godtycklig tidpunkt med större sannolikhet att vara densamma som vid tidigare klockslag. En streamingserver kan tolka, hantera och skicka data vidare enligt instruktionerna i

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18

Aussel, Nicolas. "Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL007/document.

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Avec l'augmentation du nombre de capteurs et d'actuateurs dans les avions et le développement de liaisons de données fiables entre les avions et le sol, il est devenu possible d'améliorer la sécurité et la fiabilité des systèmes à bord en appliquant des techniques d'analyse en temps réel. Cependant, étant donné la disponibilité limité des ressources de calcul embarquées et le coût élevé des liaisons de données, les solutions architecturelles actuelles ne peuvent pas exploiter pleinement toutes les ressources disponibles, limitant leur précision.Notre but est de proposer un algorithme distribué

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19

Agarwal, Virat. "Algorithm design on multicore processors for massive-data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34839.

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Analyzing massive-data sets and streams is computationally very challenging. Data sets in systems biology, network analysis and security use network abstraction to construct large-scale graphs. Graph algorithms such as traversal and search are memory-intensive and typically require very little computation, with access patterns that are irregular and fine-grained. The increasing streaming data rates in various domains such as security, mining, and finance leaves algorithm designers with only a handful of clock cycles (with current general purpose computing technology) to process every incoming

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Aussel, Nicolas. "Real-time anomaly detection with in-flight data : streaming anomaly detection with heterogeneous communicating agents." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL007.

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Avec l'augmentation du nombre de capteurs et d'actuateurs dans les avions et le développement de liaisons de données fiables entre les avions et le sol, il est devenu possible d'améliorer la sécurité et la fiabilité des systèmes à bord en appliquant des techniques d'analyse en temps réel. Cependant, étant donné la disponibilité limité des ressources de calcul embarquées et le coût élevé des liaisons de données, les solutions architecturelles actuelles ne peuvent pas exploiter pleinement toutes les ressources disponibles, limitant leur précision.Notre but est de proposer un algorithme distribué

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21

Markou, Ioannis. "Analysing User Viewing Behaviour in Video Streaming Services." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292095.

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The user experience offered by a video streaming service plays a fundamental role in customer satisfaction. This experience can be degraded by poor playback quality and buffering issues. These problems can be caused by a user demand that is higher than the video streaming service capacity. Resource scaling methods can increase the available resources to cover the need. However, most resource scaling systems are reactive and scale up in an automated fashion when a certain demand threshold is exceeded. During popular live streaming content, the demand can be so high that even by scaling up at th

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22

Zhu, Jun. "Energy and Design Cost Efficiency for Streaming Applications on Systems-on-Chip." Licentiate thesis, Stockholm : Skolan för informations- och kommunikationsteknik, Kungliga Tekniska högskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-10591.

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23

Iegorov, Oleg. "Une approche de fouille de données pour le débogage temporel des applications embarquées de streaming." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM032/document.

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Le déboggage des applications de streaming qui s'exécutent sur les systèmes embarqués multimédia est l'un des domaines les plus exigeants dans le développement de logiciel embarqué. Les nouvelles générations de materiel embarqué introduisent de nouvelles systèmes sur une puce, qui fait que les développeurs du logiciel doivent adapter leurs logiciels aux nouvelles platformes. Le logiciel embarqué doit non seulement fournir des résultats corrects mais aussi le faire en temps réel afin de respecter les propriétés de qualité de service (Quality-of-Service, QoS) du système. Lorsque les propriétés Q

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24

Ediger, David. "Analyzing hybrid architectures for massively parallel graph analysis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47659.

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The quantity of rich, semi-structured data generated by sensor networks, scientific simulation, business activity, and the Internet grows daily. The objective of this research is to investigate architectural requirements for emerging applications in massive graph analysis. Using emerging hybrid systems, we will map applications to architectures and close the loop between software and hardware design in this application space. Parallel algorithms and specialized machine architectures are necessary to handle the immense size and rate of change of today's graph data. To highlight the impact of

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Zhao, Qi. "Towards Ideal Network Traffic Measurement: A Statistical Algorithmic Approach." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19821.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2008.<br>Committee Chair: Xu, Jun; Committee Member: Ammar, Mostafa; Committee Member: Feamster, Nick; Committee Member: Ma, Xiaoli; Committee Member: Zegura, Ellen.

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Awodokun, Olugbenga. "Classification of Patterns in Streaming Data Using Clustering Signatures." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189.

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27

Zamam, Mohamad. "A unified framework for real-time streaming and processing of IoT data." Thesis, Linnéuniversitetet, Institutionen för medieteknik (ME), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-66057.

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The emergence of the Internet of Things (IoT) is introducing a new era to the realm of computing and technology. The proliferation of sensors and actuators that are embedded in things enables these devices to understand the environments and respond accordingly more than ever before. Additionally, it opens the space to unlimited possibilities for building applications that turn this sensation into big benefits, and within various domains. From smart cities to smart transportation and smart environment and the list is quite long. However, this revolutionary spread of IoT devices and technologies

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Mogis, Jay D. "Transparency, technology and trust: Music metrics and cultural distortion." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/199497/1/Jay_Mogis_Thesis.pdf.

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Transparency issues influence many aspects of modern society and are critical in balancing the dichotomy between personalisation and privacy. Transparency behaviours have previously been refined into the disclosure, clarity and accuracy (DCA) model (Schnackenberg & Tomlinson, 2014). This thesis has two projects. Project 1 makes a significant contribution to the research literature on transparency and music industry practices by testing the DCA model. Project 2 presents an investigation into music licensing, missing revenue and technology transfer in Australia. The findings suggest an accredita

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

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

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Biswas, Ayan. "Uncertainty and Error Analysis in the Visualization of Multidimensional and Ensemble Data Sets." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1480605991395144.

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31

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

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

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OUYANG, SHAO-YU, and 歐陽少佑. "Research on Live Streaming Data Analysis of University Basketball Association." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jm4sd2.

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碩士<br>輔仁大學<br>體育學系碩士班<br>107<br>The research was focused on the current live streaming situation of the college basketball league in Taiwan ,and to explore the relevant factors from the data. This research was based on a total of 64 games from the rematch to the final on the 106th academic year and the 107th academic year of the University Basketball Association men’s group first level. We used secondary data analysis method. The relevant data was collected from YouTube channel managing system. The results were proceeded by descriptive ststistics and related analysis. The conclusions are as fo

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Hsing, Hong-Yu, and 邢弘宇. "A Performance Analysis and Estimation of the Data Stream of Spark Streaming." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/7uem9b.

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碩士<br>國立臺中科技大學<br>資訊工程系碩士班<br>104<br>Since Spark Streaming handles data in a coarse-grained model (processing a micro-batch of data at a time), delays are inevitable. In the framework of Spark Streaming, data is processed after a certain amount has been collected, which aggravates the problem of delays in data processing. Such delays stem from the design of the framework. In view of that, it is worth contemplating how to calibrate the operational parameters of Spark Streaming. To put it simply, we must try to perform optimization on the processing time and the use of memory. However, it would

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Renda, Alessandro. "Algorithms and techniques for data stream mining." Doctoral thesis, 2021. http://hdl.handle.net/2158/1235915.

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The abstraction of data streams encompasses a vast range of diverse applications that continuously generate data and therefore require dedicated algorithms and approaches for exploitation and mining. In this framework both unsupervised and supervised approaches are generally employed, depending on the task and on the availability of annotated data. This thesis proposes novel algorithms and techniques specifically tailored for the streaming setting and for knowledge discovery from Social Networks. In the first part of this work we propose a novel clustering algorithm for data streams. Our inv

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Кузьо, Олег Олегович, та Oleg Kuzo. "Розробка інформаційної системи для моніторингу музичних вподобань користувачів стрімінгових сервісів". Bachelor's thesis, 2021. http://elartu.tntu.edu.ua/handle/lib/35714.

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Кваліфікаційна робота присвячена вирішенню проблеми користувача стрімінгового сервісу по аналізу його музичних вподобань. Мета роботи: створити інформаційну систему для аналізу музичних вподобань користувачів стрімінгових сервісів. В першому розділі кваліфікаційної роботи описано предмет дослідження, визначені основні складові для оцінки музичного смаку користувачів, проведено визначення необхідного функціоналу кінцевого продукту на основі існуючих аналогів, та обрано оптимальні рішення для розробки кінцевого продукту. В другому розділі кваліфікаційної роботи детально описані етапи проектува

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Ferreira, Ernesto Carlos Casanova. "Big Data Streaming Analytics." Master's thesis, 2019. http://hdl.handle.net/11110/1927.

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Big Data é o tema da ciência e tecnologia dos dias de hoje. Representa o acumular de dados em grande quantidade, heterogéneos, e a grande velocidade, verificados ao longo dos anos. Atualmente um dos desafios reside no desenvolvimento de sistemas e plataformas com capacidade para aproveitar estes dados nas diversas áreas, tais como no meio ambiente (redução da poluição, energia de renovação inteligente, redução do consumo de energia, etc.), na saúde (melhoria do bem-estar humano, qualidade de vida, etc.), na economia (previsão de negócios), na indústria (indústria 4.0, indústria inteligente, et

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Jain, A. K. "Improved Streaming Algorithm for Dyck(s) Recognition." Thesis, 2014. http://raiith.iith.ac.in/113/1/Anubhav_Kumar_jain_cs11m04.pdf.

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Keeping in mind, that any context free language can be mapped to a subset of Dyck languages and by seeing various database applications of Dyck, mainly verifying the well-formedness of XML file, we study the randomized streaming algorithms for the recognition of Dyck(s) languages, with s different types of parenthesis. The main motivation of this work is well known space bound for any T-pass streaming algorithm is (√n/T). Let x be the input stream of length n with maximum height hmax. Here we present a single-pass randomized streaming algorithms to decide the membership of x in Dyck(s)

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Shrivastava, J. M. "Streaming Algorithm for K-Median Dynamic Geometric Problem." Thesis, 2012. http://raiith.iith.ac.in/601/1/CS10M02.pdf.

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Many applications such as financial transactions data, customer click stream continuously generates huge data sets at very rapid rate. This is generally termed as data stream. These data sets are too huge to store on limited secondary storage. Therefore, it directs us to adopt a technique to maintain the sketch or summary of the data stream. This will allow us to answer any query, that we wish to perform on this data sets, approximately. Many applications on data streams such as counting of distinct items, estimating frequency moments [7, 8], the counting of frequent items [9, 3], cluster

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