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Dissertations / Theses on the topic 'Bug Processing'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Eriksson, Caroline, and Emilia Kallis. "NLP-Assisted Workflow Improving Bug Ticket Handling." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301248.

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Software companies spend a lot of resources on debugging, a process where previous solutions can help in solving current problems. The bug tickets, containing this information, are often time-consuming to read. To minimize the time spent on debugging and to make sure that the knowledge from prior solutions is kept in the company, an evaluation was made to see if summaries could make this process more efficient. Abstractive and extractive summarization models were tested for this task and fine-tuning of the bert-extractive-summarizer was performed. The model-generated summaries were compared in

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Ingvarsson, Sanne. "Using Machine Learning to Learn from Bug Reports : Towards Improved Testing Efficiency." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156711.

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The evolution of a software system originates from its changes, whether it comes from changed user needs or adaption to its current environment. These changes are as encouraged as they are inevitable, although every change to a software system comes with a risk of introducing an error or a bug. This thesis aimed to investigate the possibilities of using the description of bug reports as a decision basis for detecting the provenance of a bug by using machine learning. K-means and agglomerative clustering have been applied to free text documents by using Natural Language Processing to initially

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Bug, Daniel [Verfasser], Dorit [Akademischer Betreuer] Merhof, and Horst K. [Akademischer Betreuer] Hahn. "Digital histopathology : Image processing for histological analyses and immune response quantification / Daniel Bug ; Dorit Merhof, Horst K. Hahn." Aachen : Universitätsbibliothek der RWTH Aachen, 2020. http://d-nb.info/1240689543/34.

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4

Artchounin, Daniel. "Tuning of machine learning algorithms for automatic bug assignment." Thesis, Linköpings universitet, Programvara och system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139230.

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In software development projects, bug triage consists mainly of assigning bug reports to software developers or teams (depending on the project). The partial or total automation of this task would have a positive economic impact on many software projects. This thesis introduces a systematic four-step method to find some of the best configurations of several machine learning algorithms intending to solve the automatic bug assignment problem. These four steps are respectively used to select a combination of pre-processing techniques, a bug report representation, a potential feature selection tec

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Kasianenko, Stanislav. "Predicting Software Defectiveness by Mining Software Repositories." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78729.

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One of the important aims of the continuous software development process is to localize and remove all existing program bugs as fast as possible. Such goal is highly related to software engineering and defectiveness estimation. Many big companies started to store source code in software repositories as the later grew in popularity. These repositories usually include static source code as well as detailed data for defects in software units. This allows analyzing all the data without interrupting programing process. The main problem of large, complex software is impossibility to control everythi

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Nantes, Alfredo. "Computational approaches to the visual validation of 3D virtual environments." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48341/1/Alfredo_Nantes_Thesis.pdf.

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Virtual environments can provide, through digital games and online social interfaces, extremely exciting forms of interactive entertainment. Because of their capability in displaying and manipulating information in natural and intuitive ways, such environments have found extensive applications in decision support, education and training in the health and science domains amongst others. Currently, the burden of validating both the interactive functionality and visual consistency of a virtual environment content is entirely carried out by developers and play-testers. While considerable research

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Giordano, Manfredi. "Autonomic Big Data Processing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14837/.

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Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrast

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Rupprecht, Lukas. "Network-aware big data processing." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/52455.

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The scale-out approach of modern data-parallel frameworks such as Apache Flink or Apache Spark has enabled them to deal with large amounts of data. These applications are often deployed in large-scale data centres with many resources. However, as deployments and data continue to grow, more network communication is incurred during a data processing query. At the same time, data centre networks (DCNs) are becoming increasingly more complex in terms of the physical network topology, the variety of applications that are sharing the network, and the different requirements of these applications on t

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Lei, Chuan. "Recurring Query Processing on Big Data." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/550.

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The advances in hardware, software, and networks have enabled applications from business enterprises, scientific and engineering disciplines, to social networks, to generate data at unprecedented volume, variety, velocity, and varsity not possible before. Innovation in these domains is thus now hindered by their ability to analyze and discover knowledge from the collected data in a timely and scalable fashion. To facilitate such large-scale big data analytics, the MapReduce computing paradigm and its open-source implementation Hadoop is one of the most popular and widely used technologies. Had

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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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Echbarthi, Ghizlane. "Big Graph Processing : Partitioning and Aggregated Querying." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1225/document.

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Avec l'avènement du « big data », de nombreuses répercussions ont eu lieu dans tous les domaines de la technologie de l'information, préconisant des solutions innovantes remportant le meilleur compromis entre coûts et précision. En théorie des graphes, où les graphes constituent un support de modélisation puissant qui permet de formaliser des problèmes allant des plus simples aux plus complexes, la recherche pour des problèmes NP-complet ou NP-difficils se tourne plutôt vers des solutions approchées, mettant ainsi en avant les algorithmes d'approximations et les heuristiques alors que les solu

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Mai, Luo. "Towards efficient big data processing in data centres." Thesis, Imperial College London, 2017. http://hdl.handle.net/10044/1/64817.

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Large data processing systems require a high degree of coordination, and exhibit network bottlenecks due to massive communication data. This motivates my PhD study to propose system control mechanisms that improve monitoring and coordination, and efficient communication methods by bridging applications and networks. The first result is Chi, a new control plane for stateful streaming systems. Chi has a control loop that embeds control messages in data channels to seamlessly monitor and coordinate a streaming pipeline. This design helps monitor system and application-specific metrics in a scalab

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Wang, Jiayin. "Building Efficient Large-Scale Big Data Processing Platforms." Thesis, University of Massachusetts Boston, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10262281.

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<p> In the era of big data, many cluster platforms and resource management schemes are created to satisfy the increasing demands on processing a large volume of data. A general setting of big data processing jobs consists of multiple stages, and each stage represents generally defined data operation such as ltering and sorting. To parallelize the job execution in a cluster, each stage includes a number of identical tasks that can be concurrently launched at multiple servers. Practical clusters often involve hundreds or thousands of servers processing a large batch of jobs. Resource management,

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Fathi, Salmi Meisam. "Processing Big Data in Main Memory and on GPU." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1451992820.

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Mattasantharam, R. (Rubini). "3D web visualization of continuous integration big data." Master's thesis, University of Oulu, 2018. http://urn.fi/URN:NBN:fi:oulu-201812063239.

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Continuous Integration (CI) is a practice that is used to automate the software build and its test for every code integration to a shared repository. CI runs thousands of test scripts every day in a software organization. Every test produces data which can be test results logs such as errors, warnings, performance measurements and build metrics. This data volume tends to grow at unprecedented rates for the builds that are produced in the Continuous Integration (CI) system. The amount of the integrated test results data in CI grows over time. Visualizing and manipulating the real time and dynam

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Minarini, Francesco. "CMS processing efficiency: Big Data exploration with Kibana and Elasticsearch." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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LHC (Large Hadron Collider) ogni anno raccoglie dati, per un volume complessivo di decine di PetaBytes, provenienti da collisioni protone-protone e collisioni di ioni pesanti. In aggiunta a ciò, vanno considerati anche i dati ottenuti tramite le simulazioni Monte-Carlo. Il coordinamento delle risorse computazionali necessarie a gestire questi volumi di dati è stato raggiunto sfruttando le tecnologie fornite da WLCG (Worldwide LHC Computational Grid), una collaborazione infrastrutturale di centri di calcolo sparsi in tutto il mondo. Grazie a questa infrastruttura sono state possibili tutte le s

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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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Ozcerit, Ahmet Turan. "Fault-tolerant embedded multi-processing system with bus switching." Thesis, University of Sussex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285122.

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Kalavri, Vasiliki. "Performance Optimization Techniques and Tools for Distributed Graph Processing." Doctoral thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192471.

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In this thesis, we propose optimization techniques for distributed graph processing. First, we describe a data processing pipeline that leverages an iterative graph algorithm for automatic classification of web trackers. Using this application as a motivating example, we examine how asymmetrical convergence of iterative graph algorithms can be used to reduce the amount of computation and communication in large-scale graph analysis. We propose an optimization framework for fixpoint algorithms and a declarative API for writing fixpoint applications. Our framework uses a cost model to automatical

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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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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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Chitondo, Pepukayi David Junior. "Data policies for big health data and personal health data." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.<br>Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, eve

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Weisenseel, Chuck, and David Lane. "SIMULTANEOUS DATA PROCESSING OF MULTIPLE PCM STREAMS ON A PC BASED SYSTEM." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608317.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>The trend of current data acquisition and recording systems is to capture multiple streams of Pulse Code Modulation (PCM) data on a single media. The MARS II data recording system manufactured by Datatape, the Asynchronous Realtime Multiplexer and Output Reconstructor (ARMOR) systems manufactured by Calculex, Inc., and other systems on the market today are examples of this technology. The quantity of data recorded by these systems can be impressive, and can cause

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Da, Yanan. "A Big Spatial Data System for Efficient and Scalable Spatial Data Processing." Thesis, Southern Illinois University at Edwardsville, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10682760.

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<p> Today, a large amount of spatial data is generated from a variety of sources, such as mobile devices, sensors, and satellites. Traditional spatial data processing techniques no longer satisfy the efficiency and scalability requirements for large-scale spatial data processing. Existing Big Data processing frameworks such as Hadoop and Spark have been extended to support effective large-scale spatial data processing. In addition to processing data in distributed schemes utilizing computer clusters for efficiency and scalability, single node performance can also be improved by making use of m

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Kratz, James. "Transport phenomena in vacuum bag only prepreg processing of honeycomb sandwich panels." Thesis, McGill University, 2014. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=121325.

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Honeycomb sandwich panels offer an extremely lightweight solution for aerospace structures. As efficiency demands increase, low-cost non-autoclave manufacturing solutions are sought for honeycomb and other composite structures. Vacuum-bag-only (VBO) manufacturing is one possible solution that relies on vacuum to remove all entrapped volatiles prior to cure, and then the differential pressure between the inside and outside of the vacuum bag consolidates the layers during cure. This technique can be very effective for monolithic laminates made with out-of-autoclave (OOA) prepregs, but honeycomb

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Hossain, Mohammad. "Foundational Algorithms Underlying Horizontal Processing of Vertically Structured Big Data Using pTrees." Diss., North Dakota State University, 2016. http://hdl.handle.net/10365/25573.

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For Big Data, the time taken to process a data mining algorithm is a critical issue. Many reliable algorithms are unusable in the big data environment due to the fact that the processing takes an unacceptable amount of time. Therefore, increasing the speed of processing is very important. To address the speed issue we use horizontal processing of vertically structured data rather than the ubiquitous vertical (scan) processing of horizontal (record) data. pTree technology represents and processes data differently from the traditional horizontal data technologies. In pTree technology, the data i

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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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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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Aved, Alexander. "Scene Understanding for Real Time Processing of Queries over Big Data Streaming Video." Doctoral diss., University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5597.

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With heightened security concerns across the globe and the increasing need to monitor, preserve and protect infrastructure and public spaces to ensure proper operation, quality assurance and safety, numerous video cameras have been deployed. Accordingly, they also need to be monitored effectively and efficiently. However, relying on human operators to constantly monitor all the video streams is not scalable or cost effective. Humans can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that o

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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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Arrowsmith, Timothy William. "A NETWORK PROCESSING NODE FOR LIGHT UNMANNED AIRCRAFT." UKnowledge, 2007. http://uknowledge.uky.edu/gradschool_theses/422.

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Over the last decade, research into unmanned and autonomous vehicles has greatly increased. With applications ranging from science and exploration to humanitarian and military efforts, the rising need for autonomous vehicles demands constant innovation and growth. The Intelligent Dependable Embedded Architectures (IDEA) lab at the University of Kentucky is continually launching research oriented programs [1]. A few key projects focus on the development of Unmanned Aerial Vehicles (UAV). Through this research, at the University of Kentucky, the need to develop a reliable, lightweight, node base

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Uhlin, Jakob. "CAN signal quality analysis and development of the signal processing on a FPGA." Thesis, Linköpings universitet, Fysik och elektroteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108366.

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This master thesis report is a part of the thesis project conducted by Jakob Uhlin at Syntronic R R and D, Stockholm Sweden. The objective of this thesis is to develop a way to process the signal being sent on a CAN-bus and subsequently analyse its quality and its source in the network. A process of gathering appropriate theories and data has been done, parallel with the development of the analyzer module. The intelligence is implemented in an FPGA through the hardware description language VHDL. In this way, the algorithms can process the data in a real-time domain. The central findings and co

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Yildiz, Orcun. "Efficient Big Data Processing on Large-Scale Shared Platforms ˸ managing I/Os and Failure." Thesis, Rennes, École normale supérieure, 2017. http://www.theses.fr/2017ENSR0009/document.

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En 2017 nous vivons dans un monde régi par les données. Les applications d’analyse de données apportent des améliorations fondamentales dans de nombreux domaines tels que les sciences, la santé et la sécurité. Cela a stimulé la croissance des volumes de données (le déluge du Big Data). Pour extraire des informations utiles à partir de cette quantité énorme d’informations, différents modèles de traitement des données ont émergé tels que MapReduce, Hadoop, et Spark. Les traitements Big Data sont traditionnellement exécutés à grande échelle (les systèmes HPC et les Clouds) pour tirer parti de leu

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Tahiri, Ardit. "Online Stream Processing di Big Data su Apache Storm per Applicazioni di Instant Coupon." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10311/.

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Big data è il termine usato per descrivere una raccolta di dati così estesa in termini di volume,velocità e varietà da richiedere tecnologie e metodi analitici specifici per l'estrazione di valori significativi. Molti sistemi sono sempre più costituiti e caratterizzati da enormi moli di dati da gestire,originati da sorgenti altamente eterogenee e con formati altamente differenziati,oltre a qualità dei dati estremamente eterogenei. Un altro requisito in questi sistemi potrebbe essere il fattore temporale: sempre più sistemi hanno bisogno di ricevere dati significativi dai Big Data il prima p

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Abu, Salih Bilal Ahmad Abdal Rahman. "Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing." Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/70285.

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This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks.

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Bordin, Maycon Viana. "A benchmark suite for distributed stream processing systems." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163441.

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Um dado por si só não possui valor algum, a menos que ele seja interpretado, contextualizado e agregado com outros dados, para então possuir valor, tornando-o uma informação. Em algumas classes de aplicações o valor não está apenas na informação, mas também na velocidade com que essa informação é obtida. As negociações de alta frequência (NAF) são um bom exemplo onde a lucratividade é diretamente proporcional a latência (LOVELESS; STOIKOV; WAEBER, 2013). Com a evolução do hardware e de ferramentas de processamento de dados diversas aplicações que antes levavam horas para produzir resultados, h

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Edman, Johan, and Wilhelm Ågren. "Legal and Security Issues of Data Processing when Implementing IoT Solutions in Apartments." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-277917.

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The concept of the Internet of Things (IoT) and connected devices is a growing trend. New ways to integrate them with Smart Home Technology emerge each day. The use of sensors in IoT solutions enables large scale data collection that can be used in various ways. The European Union recently enforced a General Data Protection Regulation (GDPR) that sets guidelines for the collection and processing of personal information. The communication protocol M-Bus is a European standard (EN 13757-x) mainly used for remote reading of electrical, gas and water meters. M-Bus is being integrated with sensors

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Sabbioni, Andrea. "Stream Processing di Geo Informazioni su Apache Spark a supporto di DAE." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18843/.

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Con la rapida crescita del numero dei dispositivi mobili e delle possibilità offerte da quest’ultimi è progressivamente cambiato anche il nostro rapporto con la quotidianità. In questo contesto la capacità di associare velocemente un’informazione ad una determinata area geografica è diventato via via più rilevante in molteplici frangenti. Risultano sempre più diffuse infatti, le applicazioni geolocalizzate in diversi campi: dall’entertainment, alla pubblica utilità fino a diventare il nucleo di molti Business. Per fare fronte alle sfide appena viste abbiamo creato S2Spark un moderno framework

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Kotto, Kombi Roland. "Distributed query processing over fluctuating streams." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI050/document.

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Le traitement de flux de données est au cœur des problématiques actuelles liées au Big Data. Face à de grandes quantités de données (Volume) accessibles de manière éphémère (Vélocité), des solutions spécifiques tels que les systèmes de gestion de flux de données (SGFD) ont été développés. Ces SGFD reçoivent des flux et des requêtes continues pour générer de nouveaux résultats aussi longtemps que des données arrivent en entrée. Dans le contexte de cette thèse, qui s’est réalisée dans le cadre du projet ANR Socioplug (ANR-13-INFR-0003), nous considérons une plateforme collaborative de traitement

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Al-Sinayyid, Ali. "JOB SCHEDULING FOR STREAMING APPLICATIONS IN HETEROGENEOUS DISTRIBUTED PROCESSING SYSTEMS." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/dissertations/1868.

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The colossal amounts of data generated daily are increasing exponentially at a never-before-seen pace. A variety of applications—including stock trading, banking systems, health-care, Internet of Things (IoT), and social media networks, among others—have created an unprecedented volume of real-time stream data estimated to reach billions of terabytes in the near future. As a result, we are currently living in the so-called Big Data era and witnessing a transition to the so-called IoT era. Enterprises and organizations are tackling the challenge of interpreting the enormous amount of raw data

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Nasr, Kamil. "Comparison of Popular Data Processing Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293494.

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Data processing is generally defined as the collection and transformation of data to extract meaningful information. Data processing involves a multitude of processes such as validation, sorting summarization, aggregation to name a few. Many analytics engines exit today for largescale data processing, namely Apache Spark, Apache Flink and Apache Beam. Each one of these engines have their own advantages and drawbacks. In this thesis report, we used all three of these engines to process data from the Carbon Monoxide Daily Summary Dataset to determine the emission levels per area and unit of time

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Spence, William MacDonald. "Investigation into the opportunities presented by big data for the 4C Group." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/97409.

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Thesis (MBA)--Stellenbosch University, 2014.<br>ENGLISH ABSTRACT: The telecommunications industry generates vast amounts of data on a daily basis. The exponential growth in this industry has, therefore, increased the amounts of nodes that generates data on a near real-time basis, and the required processing power to process all this information has increased as well. Organisations in different industries have experienced the same growth in information processing, and, in recent years, professionals in the Information Systems (IS) industry have started referring to these challenges as the

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Muchemwa, Regis Fadzi. "Real-time decision support systems in a selected big data environment." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2350.

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Thesis (MTech (Business Information Systems))--Cape Peninsula University of Technology, 2016.<br>The emergence of big data (BD) has rendered existing conventional business intelligence (BI) tools inefficient and ineffective for real-time decision support systems (DSS). The inefficiency and ineffectiveness is perceived when business users need to make decisions based on stale and sometimes, incomplete data sets, which potentially leads to slow and poor decision making. In recent years, industry and academia have invented new technologies to process BD such as Hadoop, Spark, in-memory databases

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Anikwue, Arinze. "Real-time probabilistic reasoning system using Lambda architecture." Thesis, Cape Peninsula University of Technology, 2019. http://hdl.handle.net/20.500.11838/3086.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2019<br>The proliferation of data from sources like social media, and sensor devices has become overwhelming for traditional data storage and analysis technologies to handle. This has prompted a radical improvement in data management techniques, tools and technologies to meet the increasing demand for effective collection, storage and curation of large data set. Most of the technologies are open-source. Big data is usually described as very large dataset. However, a major feature of big data is its velocity. D

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Cyrus, Sam. "Fast Computation on Processing Data Warehousing Queries on GPU Devices." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6214.

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Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to process each individual query, which results in underutilization of GPU. When a single query data warehousing workload was run on an open source GPU query engine, the utilization of main GPU resources was found to be less than 25%. The low utilization then leads to low system throughput. To resolve this problem, this paper suggests a way to transfer all of the desired data into the global memory of GPU and keep it until all queries are executed as one batch. The PCIe transfer time from CPU to G

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Kumar, Anand. "Efficient and Private Processing of Analytical Queries in Scientific Datasets." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4822.

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Large amount of data is generated by applications used in basic-science research and development applications. The size of data introduces great challenges in storage, analysis and preserving privacy. This dissertation proposes novel techniques to efficiently analyze the data and reduce storage space requirements through a data compression technique while preserving privacy and providing data security. We present an efficient technique to compute an analytical query called spatial distance histogram (SDH) using spatiotemporal properties of the data. Special spatiotemporal properties present in

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Dao, Quang Minh. "High performance processing of metagenomics data." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS203.

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Avec l'avènement de la technologie de séquençage de la prochaine génération, une quantité sans cesse croissante de données génomiques est produite à mesure que le coût du séquençage diminue. Cela a permis au domaine de la métagénomique de se développer rapidement. Par conséquent, la communauté bioinformatique est confrontée à des goulots d'étranglement informatiques sans précédent pour traiter les énormes ensembles de données métagénomiques. Les pipelines traditionnels de métagénomique se composent de plusieurs étapes, utilisant différentes plates-formes de calcul distribuées et parallèles pou

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Phan, Duy-Hung. "Algorithmes d'aggrégation pour applications Big Data." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0043.

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Les bases de données traditionnelles sont confrontées à des problèmes de scalabilité et d'efficacité en raison d’importants volumes de données. Ainsi, les systèmes de gestion de base de données modernes, tels que Apache Hadoop et Spark, peuvent désormais être distribués sur des clusters de milliers de machines: ces systèmes sont donc devenus les principaux outils pour le traitement des données à grande échelle. De nombreuses optimisations ont été développées pour les bases de données conventionnelles, cependant celles-ci ne peuvent être appliquées aux nouvelles architectures et modèles de prog

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Sweeney, Michael John. "A framework for scoring and tagging NetFlow data." Thesis, Rhodes University, 2019. http://hdl.handle.net/10962/65022.

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With the increase in link speeds and the growth of the Internet, the volume of NetFlow data generated has increased significantly over time and processing these volumes has become a challenge, more specifically a Big Data challenge. With the advent of technologies and architectures designed to handle Big Data volumes, researchers have investigated their application to the processing of NetFlow data. This work builds on prior work wherein a scoring methodology was proposed for identifying anomalies in NetFlow by proposing and implementing a system that allows for automatic, real-time scoring th

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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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