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

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editeur	ESANN (i6doc.com)
Pages	421-430
Nombre de pages	10
ISBN (Electronique)	9782875870650
état	Publié - 1 janv. 2019
Modification externe	Oui
Evénement	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 - Bruges, Belgique Durée: 24 avr. 2019 → 26 avr. 2019

Série de publications

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

Une conférence

Une conférence	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019
Pays/Territoire	Belgique
La ville	Bruges
période	24/04/19 → 26/04/19

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

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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.\}",

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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. Dans 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), p. 421-430, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019, Bruges, Belgique, 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).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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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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Bifet A, Hammer B, Schleif FM. Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets. Dans 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

Résumé

Série de publications

Une conférence

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