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2019 | OriginalPaper | Chapter

A Dynamic Model + BFR Algorithm for Streaming Data Sorting

Authors : Yongwei Tan, Ling Huang, Chang-Dong Wang

Published in: Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Publisher: Springer International Publishing

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Abstract

Streaming data is widely generated in our lives. This has promoted a lot of research on streaming data mining, such as streaming data clustering and filtering. In our work, we present a problem about data stream processing, namely, streaming data sorting. There are some important characteristics of streaming data. Firstly, streaming data comes in the form of streams. It is usually assumed that streaming data is infinite, so it cannot be stored completely in memory. Secondly, we must process the streaming data in real time, otherwise we may lose the opportunity to deal with it forever. Based on these characteristics, we propose a dynamic algorithm that can make full use of memory and minimize error to solve the problem of streaming data sorting, which is further combined with the BFR algorithm to sort a particular type of streaming data. Some experiments are conducted to confirm the effectiveness of the proposed algorithms.

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Metadata
Title
A Dynamic Model + BFR Algorithm for Streaming Data Sorting
Authors
Yongwei Tan
Ling Huang
Chang-Dong Wang
Copyright Year
2019
Book
Intelligence Science and Big Data Engineering. Big Data and Machine Learning

Print ISBN: 978-3-030-36203-4

Electronic ISBN: 978-3-030-36204-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-36204-1_34

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