A Method for Stream Data Analysis

Li Zhong ()
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Li Zhong: HLRS

A chapter in Sustained Simulation Performance 2019 and 2020, 2021, pp 111-119 from Springer

Abstract: Abstract Due to the recent advances in hardware and software, a number of applications generate a huge amount of data at a great velocity which make big data stream become ubiquitous. Different from static data analysis, processing data stream imposes new challenges for algorithms and methods which need to incrementally deal with incoming data with limited memory and time. Furthermore, due to the inherent dynamic characteristics of streaming data, algorithms are often required to solve problems like concept drift, temporal dependencies, load imbalance, etc. In this paper, we discuss state of the art researches on data stream analysis which employed rigorous and methodical approaches, especially deep learning. Besides, a new method for processing data stream based on the latest development of GAN is proposed. And finally the future work to be done is discussed.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-68049-7_8

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DOI: 10.1007/978-3-030-68049-7_8

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