Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets

Albert Bifet; Barbara Hammer; Frank M. Schleif

Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets

Albert Bifet
, Barbara Hammer
, Frank M. Schleif

Université Paris-Saclay
Bielefeld University
University of Birmingham

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Today, many data are not any longer static but occur as dynamic data streams with high velocity, variability and volume. This leads to new challenges to be addressed by novel or adapted algorithms. In this tutorial we provide an introduction into the field of streaming data analysis summarizing its major characteristics and highlighting important research directions in the analysis of dynamic data.

Original language	English
Title of host publication	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	ESANN (i6doc.com)
Pages	421-430
Number of pages	10
ISBN (Electronic)	9782875870650
Publication status	Published - 1 Jan 2019
Externally published	Yes
Event	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 - Bruges, Belgium Duration: 24 Apr 2019 → 26 Apr 2019

Publication series

Name	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019
Country/Territory	Belgium
City	Bruges
Period	24/04/19 → 26/04/19

Cite this

Bifet, A., Hammer, B., & Schleif, F. M. (2019). Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. In ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 421-430). (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). ESANN (i6doc.com).

Bifet, Albert ; Hammer, Barbara ; Schleif, Frank M. / Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), 2019. pp. 421-430 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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title = "Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets",

abstract = "Today, many data are not any longer static but occur as dynamic data streams with high velocity, variability and volume. This leads to new challenges to be addressed by novel or adapted algorithms. In this tutorial we provide an introduction into the field of streaming data analysis summarizing its major characteristics and highlighting important research directions in the analysis of dynamic data.",

author = "Albert Bifet and Barbara Hammer and Schleif, \{Frank M.\}",

note = "Publisher Copyright: {\textcopyright} 2019 ESANN (i6doc.com). All rights reserved.; 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2019",

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language = "English",

series = "ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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Bifet, A, Hammer, B & Schleif, FM 2019, Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. in ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com), pp. 421-430, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019, Bruges, Belgium, 24/04/19.

Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. / Bifet, Albert; Hammer, Barbara; Schleif, Frank M.
ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), 2019. p. 421-430 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hammer, Barbara

AU - Schleif, Frank M.

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N2 - Today, many data are not any longer static but occur as dynamic data streams with high velocity, variability and volume. This leads to new challenges to be addressed by novel or adapted algorithms. In this tutorial we provide an introduction into the field of streaming data analysis summarizing its major characteristics and highlighting important research directions in the analysis of dynamic data.

AB - Today, many data are not any longer static but occur as dynamic data streams with high velocity, variability and volume. This leads to new challenges to be addressed by novel or adapted algorithms. In this tutorial we provide an introduction into the field of streaming data analysis summarizing its major characteristics and highlighting important research directions in the analysis of dynamic data.

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M3 - Conference contribution

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Bifet A, Hammer B, Schleif FM. Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. In ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com). 2019. p. 421-430. (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets

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