Balan Sethu Raman, Mohamed Ali · 2010
Have you ever counted the number of times the word "streaming" has occurred in a geospatial oriented conference proceedings over the past few years? Have you ever monitored the growth of the geospatial research and industrial community? Have you ever noticed that geospatial researchers are living the luxury of an era where real-time data is streamed at the convenience of their fingertips? Thanks to advances in the "geosensing" technologies, the geospatial community is "almost" ready for their next revolutionary jump. But not quite ready yet! Until we understand how we can process, mine, and analyze the massive amount of data being streamed from geo-sensors every second, the challenge is still there.
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Raed Alsini, Xiaogang Ma · 2019
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2008
Recent work on querying data streams has focused on systems where newly arriving data is processed and continuously streamed to the user in real-time. In many emerging applications, however, ad hoc queries and/or intermittent connectivity also require the processing of data that arrives prior to query submission or during a period of disconnection. For such applications, we have developed PSoup, a system that combines the processing of ad-hoc and continuous queries by treating data and queries symmetrically, allowing new queries to be applied to old data and new data to be applied to old queries. PSoup also supports intermittent connectivity by separating the computation of query results from the delivery of those results. PSoup builds on adaptive query processing techniques developed in the Telegraph project at UC Berkeley. In this paper, we describe PSoup and present experiments that demonstrate the effectiveness of our approach. 1
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Alex Endert, William Pike, Kristin Cook · 2014
The rise of Big Data has influenced the design and technical implementation of visual analytic tools required to handle the increased volumes, velocities, and varieties of data. This has required a set of data management and computational advancements to allow us to store and compute on such datasets. However, as the ultimate goal of visual analytic technology is to enable the discovery and creation of insights from the users, an under-explored area is understanding how these datasets impact their mental models. That is, how have the analytic processes and strategies of users changed? How have users changed their perception of how to leverage, and ask questions of, these datasets?
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P. V Srilakshmi Annapoorna, T. T. Mirnalinee · 2016
In the evolving technology of big data, high velocity data streams play a vital role since pattern of data is being changed over time. The temporal pattern change in data stream leads to a concept evolution called concept drift where statistical properties of data differs from time to time and the drift is taken into account in order to update old and outdated classifier and make it adaptable to new data arrival and pattern change over. In order to classify the stream data, a scalable efficient classification algorithm is to be designed which perfectly classifies the data with minimizing misclassification rate in presence of concept drift due to high velocity data. Training time of the classifier must be reduced in order to reduce computational complexity. In this work, a novel algorithm has been implemented using Random Forest with stratified random sampling and Bloom filtering in order to reduce the training time and to handle high velocity data. Experimental results are shown by performing classification with sampling, classification with filtering and classification with sampling and filtering. This enhances the performance of the algorithm by decreasing the training time and testing time of the classifier with negligible compromise in accuracy of classification.
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Alexander Lemzin · Asian Journal of Research in Computer Science · 2023
Aims: The data which is continuously being produced by hundreds of thousands of data sources is recognized as streamed data. The data which is processed via this kind of source is relatively smaller in size and is being sent at the same time it is generated.
 Study Design: In streaming data, the data range is so wide like the telemetry from interconnected devices or other such forms of data with the inclusion of certain web applications. This information should be handled consecutively and steadily on a record-by-record premise or throughout sliding time windows and utilized for a wide assortment of examinations including relationships, totals, separating, and inspecting.
 Place and Duration of Study: Service usage (for metering and billing), server activity, website clicks, and the geo-location of devices, people, and physical goods are just a few of the many aspects of a company's business and customer activity that can be seen through this type of analysis. It also enables companies to respond quickly to new arising situations.
 Methodology: The research methodology is used for the current research work is the qualitative method through which the research studies of a similar domain are studied thoroughly. It has been analyzed that the specified changes in the large volumes of data can better be managed through stream data processing.
 Results: The flaws of batch data processing are better dealt with through the usage of streaming data processing agenda. Real-time monitoring as well as response functionality are the keys to success in the given method of data processing.
 Conclusion: Stream data processing connects analytics and applications. Because multiple systems can be constructed using the same architecture, this makes the construction of the infrastructure a similar architecture. It additionally allows designers to fabricate applications that utilize scientific outcomes to straightforwardly answer information experiences and make a move.
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Max Bramer · Undergraduate topics in computer science · 2016
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Aragues, Anthony · CERN Document Server (European Organization for Nuclear Research) · 2018
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EdShare Southampton (University of Southampton)
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Jack Y. B. Lee · 2005
This chapter contains sections titled: Streaming over TCP/UDP Specialized Streaming Protocols Summary References
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Max Bramer · Undergraduate topics in computer science · 2020
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Riccardo Tommasini, Pieter Bonte, Fabiano Spiga et al. · 2023
This book provides a comprehensive overview of core concepts and technological foundations for continuous engineering of Web streams.
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Riccardo Tommasini · Encyclopedia of Big Data Technologies · 2022
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Frank W. Miller, Peter J. Keleher, Satish K. Tripathi · 2002
This work presents a new I/O system design and implementation targeted at applications that perform data streaming. The approach yields true zero-copy transfers between I/O devices in many instances. We give a general characterization of I/O elements and provide a framework that allows analysis of the potential for zero-copy transfers. Finally, we describe the design, implementation, and performance of a prototype I/O system in a real time, embeddable, 32-bit operating system whose design is based on the presented analysis to minimize data copying.
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Luca Becchetti · 2012
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Ferreira, Ernesto Carlos Casanova · CiencIPCA · 2020
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, etc.), entre muitos outros. Estes objetivos são possíveis de atingir através de: i) recolha; ii) tratamento; iii) análise com consequente ação.\nO desenvolvimento de uma plataforma que permita analisar dados em streaming provenientes de dispositivos, rede de sensores com fio sem fio, web, feeds de redes sociais, aplicações e muito mais, representa um grande desafio e uma ótima oportunidade, adicionando a capacidade de extrair conhecimento de fluxos de dados em tempo real, identificando padrões e relações, apoiar decisões e prever comportamentos, são objetivos considerados.\nA gestão de energia representa o cenário onde essas realidades coexistem, ou seja, há muitos dados já armazenados e muitos mais gerados continuamente, onde o estado atual e a previsão (monitorização e controlo) representam ganhos e bases para melhores decisões, ao nível do consumo, da rentabilidade, da eficiência energética, da segurança, etc. Estes dados, pelo seu aumento substancial todos os anos, leva a desenvolver uma tecnologia que permita acompanhar este crescimento.\nNeste âmbito, com este trabalho pretendeu-se desenvolver uma arquitetura de Streaming que permita a recolha e análise em Big Data para otimização de processos de negócio com um caso de estudo baseado em gestão de energia que permita a análise de dados de sensores em tempo real ou próximo de tempo real. No desenvolvimento desta plataforma, foi objetivo combinar várias tecnologias, que permita entre outros: i) analisar dados de utilidade elevada em tempo real; ii) otimizar custos com os recursos de hardware em instalação On Premises; iii) flexibilidade, permitindo ser aplicável a vários casos de uso; iiii) contribuir para o desenvolvimento de um produto.
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Ye Tian, Min Zhao, Xinming Zhang · SpringerBriefs in computer science · 2017
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Gottfried Vossen, Till Haselmann · 2013
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Wang, Fan · Data Archiving and Networked Services (DANS) · 2024
Live streaming data was collected in the U.S. and China and titled "combined data (final)", while the experimental data for study 2 was titled "live stream co-experience experiment—0717 (916)".
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Kevin Feasel · Apress eBooks · 2020
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