research-article

Open challenges for data stream mining research

Authors:

Indre Žliobaite,

Dariusz Brzeziński,

Eyke Hüllermeier,

Vincent Lemaire,

Myra Spiliopoulou,

Jerzy StefanowskiAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 16, Issue 1

Pages 1 - 10

https://doi.org/10.1145/2674026.2674028

Published: 25 September 2014 Publication History

Abstract

Every day, huge volumes of sensory, transactional, and web data are continuously generated as streams, which need to be analyzed online as they arrive. Streaming data can be considered as one of the main sources of what is called big data. While predictive modeling for data streams and big data have received a lot of attention over the last decade, many research approaches are typically designed for well-behaved controlled problem settings, overlooking important challenges imposed by real-world applications. This article presents a discussion on eight open challenges for data stream mining. Our goal is to identify gaps between current research and meaningful applications, highlight open problems, and define new application-relevant research directions for data stream mining. The identified challenges cover the full cycle of knowledge discovery and involve such problems as: protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms. The resulting analysis is illustrated by practical applications and provides general suggestions concerning lines of future research in data stream mining.

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Cited By

Hu HKantardzic MKar S(2024)Explainable data stream miningIntelligent Decision Technologies10.3233/IDT-23006518:1(371-385)Online publication date: 1-Jan-2024
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Index Terms

Open challenges for data stream mining research
1. Information systems
  1. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 16, Issue 1

Special issue on big data

June 2014

63 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/2674026

Editors:
Charu C. Aggarwal,
Haixun Wang,
Hanghang Tong,
Ankur Teredesai
University of Washington, Seattle, Washington

Issue’s Table of Contents

Copyright © 2014 Authors.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2014

Published in SIGKDD Volume 16, Issue 1

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Cited By

Hu HKantardzic MKar S(2024)Explainable data stream miningIntelligent Decision Technologies10.3233/IDT-23006518:1(371-385)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/IDT-230065
Zhou PGuo YYu HYan YZhang YWu X(2024)Concept Evolution Detecting over Feature StreamsACM Transactions on Knowledge Discovery from Data10.1145/367801218:8(1-32)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3678012
Sun JDu MLew ZDong Y(2024)TWStream: Three-Way Stream ClusteringIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.336971632:9(4927-4939)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3369716
Fu YLiu ZWu CWu FLiu M(2024)Class-Incremental Recognition of Objects in Remote Sensing Images With Dynamic Hybrid Exemplar SelectionIEEE Transactions on Aerospace and Electronic Systems10.1109/TAES.2024.336311460:3(3468-3481)Online publication date: Jun-2024
https://doi.org/10.1109/TAES.2024.3363114
Wu YWu HJia CPeng BZhang ZYang TChen PYang KCui B(2024)Scalable Overspeed Item Detection in Streams2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00094(1157-1170)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00094
Hofer VKrempl GLang D(2024)Anticipative Bayesian classification for data streams with verification latencyJournal of Applied Statistics10.1080/02664763.2024.2319222(1-20)Online publication date: 21-Feb-2024
https://doi.org/10.1080/02664763.2024.2319222
Usman MChen H(2024)Bin.INI: An ensemble approach for dynamic data streamsExpert Systems with Applications10.1016/j.eswa.2024.124853256(124853)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124853
Ullah IAdhikari DSu XPalmieri FWu CChoi C(2024)Integration of data science with the intelligent IoT (IIoT): current challenges and future perspectivesDigital Communications and Networks10.1016/j.dcan.2024.02.007Online publication date: Mar-2024
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Panjei EGruenwald L(2024)Discovering outlying attributes of outliers in data streamsData & Knowledge Engineering10.1016/j.datak.2024.102349(102349)Online publication date: Aug-2024
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de Sousa RMeira Neto AFantinato MPeres SReijers H(2024)Integrated detection and localization of concept drifts in process mining with batch and stream trace clustering supportData & Knowledge Engineering10.1016/j.datak.2023.102253149(102253)Online publication date: Jan-2024
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