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Relevant bibliographies by topics / Online data stream processing / Dissertations / Theses

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Dissertations / Theses on the topic 'Online data stream processing'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ahmed, Abdulbasit. "Online network intrusion detection system using temporal logic and stream data processing." Thesis, University of Liverpool, 2013. http://livrepository.liverpool.ac.uk/12153/.

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These days, the world is becoming more interconnected, and the Internet has dominated the ways to communicate or to do business. Network security measures must be taken to protect the organization environment. Among these security measures are the intrusion detection systems. These systems aim to detect the actions that attempt to compromise the confidentiality, availability, and integrity of a resource by monitoring the events occurring in computer systems and/or networks. The increasing amounts of data that are transmitted at higher and higher speed networks created a challenging problem for

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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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VASCONCELOS, IGOR OLIVEIRA. "A MOBILE AND ONLINE OUTLIER DETECTION OVER MULTIPLE DATA STREAMS: A COMPLEX EVENT PROCESSING APPROACH FOR DRIVING BEHAVIOR DETECTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30648@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Dirigir é uma tarefa diária que permite uma locomoção mais rápida e mais confortável, no entanto, mais da metade dos acidentes fatais estão relacionados à imprudência. Manobras imprudentes podem ser detectadas com boa precisão, analisando dados relativos à interação motorista-veículo, por exemplo, curvas, aceleração e desaceleração abruptas. Embora existam algoritmos para detecç

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Silva, Ticiana Linhares Coelho da. "Online clustering of trajectory data stream." reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/22045.

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SILVA, Ticiana Linhares Coelho da. Online clustering of trajectory data stream. 2016. 113 f. Tese (Doutorado em Ciência da Computação)-Universidade Federal do Ceará, Fortaleza, 2016.<br>Submitted by Jairo Viana (jairo@ufc.br) on 2017-02-17T18:32:07Z No. of bitstreams: 1 2016_tese_tlcsilva.pdf: 21709584 bytes, checksum: 1454aec7cf746b2ad56eda5865264deb (MD5)<br>Approved for entry into archive by Jairo Viana (jairo@ufc.br) on 2017-02-17T18:32:27Z (GMT) No. of bitstreams: 1 2016_tese_tlcsilva.pdf: 21709584 bytes, checksum: 1454aec7cf746b2ad56eda5865264deb (MD5)<br>Made available in DSpace on 2017

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Schmidt, Sven. "Quality of service aware data stream processing." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=983780625.

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Vijayakumar, Nithya Nirmal. "Data management in distributed stream processing systems." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278228.

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Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2007.<br>Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).

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Schmidt, Sven. "Quality-of-Service-Aware Data Stream Processing." Doctoral thesis, Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A23955.

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Data stream processing in the industrial as well as in the academic field has gained more and more importance during the last years. Consider the monitoring of industrial processes as an example. There, sensors are mounted to gather lots of data within a short time range. Storing and post-processing these data may occasionally be useless or even impossible. On the one hand, only a small part of the monitored data is relevant. To efficiently use the storage capacity, only a preselection of the data should be considered. On the other hand, it may occur that the volume of incoming data is general

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Bai, Yijian. "Data stream processing and query optimization techniques." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1472132461&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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9

Chen, Jian. "Maintaining Stream Data Distribution Over Sliding Window." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35321.

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In modern applications, it is a big challenge that analyzing the order statistics about the most recent parts of the high-volume and high velocity stream data. There are some online quantile algorithms that can keep the sketch of the data in the sliding window and they can answer the quantile or rank query in a very short time. But most of them take the GK algorithm as the subroutine, which is not known to be mergeable. In this paper, we propose another algorithm to keep the sketch that maintains the order statistics over sliding windows. For the fixed-size window, the existing algorithms can’

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Works, Karen E. "Targeted Prioritized Processing in Overloaded Data Stream Systems." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/414.

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"We are in an era of big data, sensors, and monitoring technology. One consequence of this technology is the continuous generation of massive volumes of streaming data. To support this, stream processing systems have emerged. These systems must produce results while meeting near-real time response obligations. However, computation intensive processing on high velocity streams is challenging. Stream arrival rates are often unpredictable and can fluctuate. This can cause systems to not always be able to process all incoming data within their required response time.Yet inherently some results may

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Drougas, Ioannis. "Rate allocation in distributed stream processing systems." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077971&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268240766&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2008.<br>Includes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 93-98). Also issued in print.

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Mousavi, Bamdad. "Scalable Stream Processing and Management for Time Series Data." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42295.

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There has been an enormous growth in the generation of time series data in the past decade. This trend is caused by widespread adoption of IoT technologies, the data generated by monitoring of cloud computing resources, and cyber physical systems. Although time series data have been a topic of discussion in the domain of data management for several decades, this recent growth has brought the topic to the forefront. Many of the time series management systems available today lack the necessary features to successfully manage and process the sheer amount of time series being generated today. In t

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Derakhshan, Roozbeh. "Towards Stream-Relation Join Processing in Data Streaming Engines." Thesis, Griffith University, 2016. http://hdl.handle.net/10072/365833.

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We are living in a time where a massive 2.5 quintillion bytes of data is generated every day. To realise the value of this data, stream data processing offers a new pro- cessing paradigm that aggregates and analyses large volumes of data quickly. While several commercial Stream Processing Engines (SPEs) are available, it remains dif- ficult to develop stream-based applications. Over the last decade our research has identified and addressed two dominant reasons for this difficulty: Heterogeneity and Stored-Streaming Divide. Heterogeneity highlights the lack of standards in SPEs as well as the w

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Stein, Oliver. "Intelligent Resource Management for Large-scale Data Stream Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-391927.

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With the increasing trend of using cloud computing resources, the efficient utilization of these resources becomes more and more important. Working with data stream processing is a paradigm gaining in popularity, with tools such as Apache Spark Streaming or Kafka widely available, and companies are shifting towards real-time monitoring of data such as sensor networks, financial data or anomaly detection. However, it is difficult for users to efficiently make use of cloud computing resources and studies show that a lot of energy and compute hardware is wasted. We propose an approach to optimizi

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Lee, Lap-kei, and 李立基. "New results on online job scheduling and data stream algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42182451.

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Lee, Lap-kei. "New results on online job scheduling and data stream algorithms." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42182451.

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Nguyen, Thi Thu Thuy. "Advanced machine learning techniques for online and data stream learning." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386536.

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Big Data is the certain result of our knowledge-intensive world when practically everything is being monitored and measured. According to the 2014 report of the International Data Corporation (IDC), the amount of information created, captured or replicated had exceeded available storage for the first time in 2007. The digital universe is doubling in size every two years and will multiply 10-fold between 2013 and 2020 – from 4.4 trillion gigabytes to 44 trillion gigabytes. Besides the massive volume, the velocity of data is another concern as in our information society, very often data come in

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Poppe, Olga. "Event stream analytics." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-dissertations/530.

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Advances in hardware, software and communication networks have enabled applications to generate data at unprecedented volume and velocity. An important type of this data are event streams generated from financial transactions, health sensors, web logs, social media, mobile devices, and vehicles. The world is thus poised for a sea-change in time-critical applications from financial fraud detection to health care analytics empowered by inferring insights from event streams in real time. Event processing systems continuously evaluate massive workloads of Kleene queries to detect and aggregate eve

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Pham, Cuong X. "Advanced techniques for data stream analysis and applications." Thesis, Griffith University, 2023. http://hdl.handle.net/10072/421691.

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Deep learning (DL) is one of the most advanced AI techniques that has gained much attention in the last decade and has also been applied in many successful applications such as market stock prediction, object detection, and face recognition. The rapid advances in computational techniques like Graphic Processing Units (GPU) and Tensor Processing Units (TPU) have made it possible to train large deep learning models to obtain high accuracy surpassing human ability in some tasks, e.g., LipNet [9] achieves 93% of accuracy compared with 52% of human to recognize the word from speaker lips movement.

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Gu, Wenyu. "Improving the performance of stream processing pipeline for vehicle data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284547.

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The growing amount of position-dependent data (containing both geo position data (i.e. latitude, longitude) and also vehicle/driver-related information) collected from sensors on vehicles poses a challenge to computer programs to process the aggregate amount of data from many vehicles. While handling this growing amount of data, the computer programs that process this data need to exhibit low latency and high throughput – as otherwise the value of the results of this processing will be reduced. As a solution, big data and cloud computing technologies have been widely adopted by industry. This

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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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Xu, Cheng. "Scalable Validation of Data Streams." Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-291530.

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In manufacturing industries, sensors are often installed on industrial equipment generating high volumes of data in real-time. For shortening the machine downtime and reducing maintenance costs, it is critical to analyze efficiently this kind of streams in order to detect abnormal behavior of equipment. For validating data streams to detect anomalies, a data stream management system called SVALI is developed. Based on requirements by the application domain, different stream window semantics are explored and an extensible set of window forming functions are implemented, where dynamic registrati

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Joe-Yen, Stefan. "Performance Envelopes of Adaptive Ensemble Data Stream Classifiers." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1014.

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This dissertation documents a study of the performance characteristics of algorithms designed to mitigate the effects of concept drift on online machine learning. Several supervised binary classifiers were evaluated on their performance when applied to an input data stream with a non-stationary class distribution. The selected classifiers included ensembles that combine the contributions of their member algorithms to improve overall performance. These ensembles adapt to changing class definitions, known as “concept drift,” often present in real-world situations, by adjusting the relative contr

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Yates, James William. "Mixing Staged Data Flow and Stream Computing Techniques in Modern Telemetry Data Acquisition/Processing Architectures." International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608707.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada<br>Today’s flight test processing systems must handle many more complex data formats than just the PCM and analog FM data streams of yesterday. Many flight test programs, and their respective test facilities, are looking to leverage their computing assets across multiple customers and programs. Typically, these complex programs require the ability to handle video, packet, and avionics bus data in real time, in addition to handling the more traditional PCM format. Cur

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Robakowski, Mikolaj. "Comparison of State Backends for Modern Stream Processing System." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290597.

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Distributed Stream Processing is a very popular computing paradigm used invarious modern computer systems. An important aspect of distributed streamprocessing systems is how they deal with computation state bigger than thesystem memory. This is often solved by the usage of a state backend – adatabase, usually an embedded one, that manages the state on the persistentstorage. However, this makes the performance of the whole system dependanton the performance of the database under the given workload. Log-structuredmerge-tree-based solutions are commonly used in stream processing systemsas a one-s

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Fernández, Moctezuma Rafael J. "A Data-Descriptive Feedback Framework for Data Stream Management Systems." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/116.

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Data Stream Management Systems (DSMSs) provide support for continuous query evaluation over data streams. Data streams provide processing challenges due to their unbounded nature and varying characteristics, such as rate and density fluctuations. DSMSs need to adapt stream processing to these changes within certain constraints, such as available computational resources and minimum latency requirements in producing results. The proposed research develops an inter-operator feedback framework, where opportunities for run-time adaptation of stream processing are expressed in terms of descriptions

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Wladdimiro, Cottet Daniel. "Dynamic adaptation in Stream Processing Systems." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS028.

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Le nombre de données produites par les systèmes ou applications Web actuels augmente rapidement en raison des nombreuses interactions avec les utilisateurs (dans le cadre par exemple, transactions boursières en temps réel, des jeux multijoueurs, des données en continu produits par Twitter, etc.). Ainsi, il existe une demande croissante, notamment dans les domaines du commerce, de la sécurité et de la recherche, pour des systèmes capables de traiter ces données en temps réel et de fournir des informations utiles dans un court laps de temps. Les systèmes de traitement des flux (SPS) répondent à

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Wang, Junxian. "Online hotel booking system." CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/3083.

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The Online Hotel Booking System was developed to allow customers to use a web browser to book a hotel, change the booking details, cancel the booking, change the personal profile, view the booking history, or view the hotel information through a GUI (graphical user interface). The system is implemented in PHP (Hypertext Preprocessor) and HTML (Hyper Text Markup Language).

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De, Souza Felipe Rodrigo. "Scheduling Solutions for Data Stream Processing Applications on Cloud-Edge Infrastructure." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN082.

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L’évolution des technologies ont conduit à une forte connexion entre les applications et le matériel produisant des quantités de données en perpétuelle augmentation. Ces données sont utilisées par les entreprises, les organisations et les individus pour prendre des décisions quotidiennes. Pour que les données collectées soient réellement utiles il convient de les traiter à temps et donc suffisamment rapidement. La vitesse à laquelle les informations sont extraites depuis les données générées par un système ou un environnement surveillé a un impact sur la capacité des entités (entreprises, orga

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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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Tamborrino, Alexandre. "A Real-Time Reactive Platform for Data Integration and Event Stream Processing." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177203.

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This thesis presents a Real-time Reactive platform for Data Integration and Event Stream Processing. The Data Integration part is composed of data pullers that incrementally pull data changes from REST data sources and propagates them as streams of immutable events across the system according to the Event-Sourcing principle. The Stream Processing part is a Tree-like structure of event-sourced stream processors where a processor can react in various ways to events sent by its parent and send derived sub-streams of events to child processors. A processor use case is maintaining a pre-computed vi

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Peiro, Sajjad Hooman. "Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers." Licentiate thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-193582.

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In this thesis, our goal is to enable and achieve effective and efficient real-time stream processing in a geo-distributed infrastructure, by combining the power of central data centers and micro data centers. Our research focus is to address the challenges of distributing the stream processing applications and placing them closer to data sources and sinks. We enable applications to run in a geo-distributed setting and provide solutions for the network-aware placement of distributed stream processing applications across geo-distributed infrastructures. First, we evaluate Apache Storm, a widel

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Ji, Yuanzhen. "Handling Tradeoffs between Performance and Query-Result Quality in Data Stream Processing." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-233997.

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Data streams in the form of potentially unbounded sequences of tuples arise naturally in a large variety of domains including finance markets, sensor networks, social media, and network traffic management. The increasing number of applications that require processing data streams with high throughput and low latency have promoted the development of data stream processing systems (DSPS). A DSPS processes data streams with continuous queries, which are issued once and return query results to users continuously as new tuples arrive. For stream-based applications, both the query-execution perform

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Alrammal, Muath. "Algorithms for XML stream processing : massive data, external memory and scalable performance." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00779309.

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Plusieurs applications modernes nécessitent un traitement de flux massifs de données XML, cela crée de défis techniques. Parmi ces derniers, il y a la conception et la mise en ouvre d'outils pour optimiser le traitement des requêtes XPath et fournir une estimation précise des coûts de ces requêtes traitées sur un flux massif de données XML. Dans cette thèse, nous proposons un nouveau modèle de prédiction de performance qui estime a priori le coût (en termes d'espace utilisé et de temps écoulé) pour les requêtes structurelles de Forward XPath. Ce faisant, nous réalisons une étude expérimentale

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VASCONCELOS, RAFAEL OLIVEIRA. "A DYNAMIC LOAD BALANCING MECHANISM FOR DATA STREAM PROCESSING ON DDS SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23629@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Esta dissertação apresenta a solução de balanceamento de carga baseada em fatias de processamento de dados (Data Processing Slice Load Balancing solution) para permitir o balanceamento de carga dinâmico do processamento de fluxos de dados em sistemas baseados em DDS (Data Distribution Service). Um grande número de aplicações requer o processamento contínuo de alto volume de dados oriundos de várias fontes distribuídas., tais como monitorament

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Georgiadis, Charalampos. "An evaluation and performance comparison of different approaches for data stream processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-307214.

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In recent years the demand of faster data processing and real-time analysis and reporting has grown substantially. Social networks, internet of things, trading are among others, use cases where data stream processing has a vital importance. This has led to the emergence of several distributed computing frameworks that can be successfully exploited for data stream processing purposes. This project aims to examine a number of them, their architecture and key features. First, all the open source frameworks were found and studied. Based on the approach they follow, two of them were selected to be

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Tallberg, Sebastian. "A COMPARISON OF DATA INGESTION PLATFORMS IN REAL-TIME STREAM PROCESSING PIPELINES." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-48744.

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In recent years there has been an increasing demand for real-time streaming applications that handle large volumes of data with low latency. Examples of such applications include real-time monitoring and analytics, electronic trading, advertising, fraud detection, and more. In a streaming pipeline the first step is ingesting the incoming data events, after which they can be sent off for processing. Choosing the correct tool that satisfies application requirements is an important technical decision that must be made. This thesis focuses entirely on the data ingestion part by evaluating three di

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Scott, Stacey P. "DEVELOPING ONLINE HELP AT BLUESPRING SOFTWARE: AN INTERNSHIP." Oxford, Ohio : Miami University, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1101930066.

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Kardos, Péter. "Performance optimization ofthe online data processing softwareof a high-energy physics experiment : Performance optimization ofthe online data processing softwareof a high-energy physics experiment." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-404475.

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The LHCb experiment probes the differences between matter and anti-matter by examining particle collisions. Like any modern high energy physics experiment, LHCbrelies on a complex hardware and software infrastructure to collect and analyze the data generated from particle collisions. To filter out unimportant data before writing it to permanent storage, particle collision events have to be processed in real-time which requires a lot of computing power. This thesis focuses on performance optimizations of several parts of the real-timedata processing software: i) one of the particle path reconst

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Al, Jawarneh Isam Mashhour Hasan <1981&gt. "Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amsdottorato.unibo.it/9402/1/PhD-Thesis-ALJAWARNEH.pdf.

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Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. T

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Popa, Tiberiu. "Compiling Data Dependent Control Flow on SIMD GPUs." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/1186.

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Current Graphic Processing Units (GPUs) (circa. 2003/2004) have programmable vertex and fragment units. Often these units are implemented as SIMD processors employing parallel pipelines. Data dependent conditional execution on SIMD architectures implemented using processor idling is inefficient. I propose a multi-pass approach based on conditional streams which allows dynamic load balancing of the fragment units of the GPU and better theoretical performance on programs using data dependent conditionals and loops. The proposed system can be used to turn the fragment unit of

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Ives, Zachary G. "Efficient query processing for data integration /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6864.

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Vossough, Ehsan. "Processing of continuous queries over infinite data streams." Access electronically, 2004. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20050112.154300/index.html.

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Hung, Yee-shing Regant, and 洪宜成. "Scheduling online batching systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B34624016.

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Ding, Luping. "Metadata-aware query processing over data streams." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-042208-194826/.

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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data s

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Belcin, Andrei. "Smart Cube Predictions for Online Analytic Query Processing in Data Warehouses." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41956.

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A data warehouse (DW) is a transformation of many sources of transactional data integrated into a single collection that is non-volatile and time-variant that can provide decision support to managerial roles within an organization. For this application, the database server needs to process multiple users’ queries by joining various datasets and loading the result in main memory to begin calculations. In current systems, this process is reactionary to users’ input and can be undesirably slow. In previous studies, it was shown that a personalization scheme of a single user’s query patterns an

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Addimando, Alessio. "Progettazione e prototipazione di un sistema di Data Stream Processing basato su Apache Storm." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10977/.

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Con l’avvento di Internet, il numero di utenti con un effettivo accesso alla rete e la possibilità di condividere informazioni con tutto il mondo è, negli anni, in continua crescita. Con l’introduzione dei social media, in aggiunta, gli utenti sono portati a trasferire sul web una grande quantità di informazioni personali mettendoli a disposizione delle varie aziende. Inoltre, il mondo dell’Internet Of Things, grazie al quale i sensori e le macchine risultano essere agenti sulla rete, permette di avere, per ogni utente, un numero maggiore di dispositivi, direttamente collegati tra loro e alla

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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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Chen, Yaming. "Chinese Restaurant Online System." CSUSB ScholarWorks, 2005. https://scholarworks.lib.csusb.edu/etd-project/2833.

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Chinese Restaurant Online System was designed to allow customers to view information about the restaurant over the Web and order some dishes online. It allows employees to login to do their work, depending on their sponsored rights.

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