World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
8405
World Ranking
12403
National Ranking
5028

Overview

Abdullah Mueen is affiliated with the University of New Mexico in the United States and specializes in computer science, with a focus on artificial intelligence, signal processing, and information systems. Their body of work includes contributions to the study of time series analysis and forecasting, anomaly detection techniques, and data stream mining techniques.

The scientist has published extensively on topics including:

  • Time Series Analysis and Forecasting
  • Anomaly Detection Techniques and Applications
  • Data Stream Mining Techniques
  • Topic Modeling
  • Seismology and Earthquake Studies
  • AI in Service Interactions
  • Spam and Phishing Detection

Frequent publication venues for Abdullah Mueen include:

  • Data Mining and Knowledge Discovery
  • Proceedings of the International Florida Artificial Intelligence Research Society Conference
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • AI Magazine

Several coauthors have collaborated with Abdullah Mueen multiple times. These collaborators include:

  • Ian Beaver
  • Cynthia Freeman
  • Vinícius M. A. Souza
  • Eamonn Keogh
  • Farhan Asif Chowdhury

The scientist's recent papers demonstrate a focus on large-scale and accurate time series anomaly detection methods as well as social media analysis. Selected recent publications are:

  • Matrix Profile XXIV: Scaling Time Series Anomaly Detection to Trillions of Datapoints and Ultra-fast Arriving Data Streams (2022), Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code (2020), Data Mining and Knowledge Discovery
  • On Twitter Purge: A Retrospective Analysis of Suspended Users (2020), Companion Proceedings of the Web Conference 2020
  • Experimental Comparison and Survey of Twelve Time Series Anomaly Detection Algorithms (2021), Journal of Artificial Intelligence Research
  • DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams (2023), Data Mining and Knowledge Discovery

Best Publications

  • Searching and mining trillions of time series subsequences under dynamic time warping

    Thanawin Rakthanmanon;Bilson Campana;Abdullah Mueen;Gustavo Batista

  • Experimental comparison of representation methods and distance measures for time series data

    Xiaoyue Wang;Abdullah Mueen;Hui Ding;Goce Trajcevski

  • Curse of Dimensionality.

    Eamonn J. Keogh;Abdullah Mueen

  • Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

    Chin-Chia Michael Yeh;Yan Zhu;Liudmila Ulanova;Nurjahan Begum

  • Exact Discovery of Time Series Motifs.

    Abdullah Mueen;Eamonn J. Keogh;Qiang Zhu;Sydney Cash

  • Logical-shapelets: an expressive primitive for time series classification

    Abdullah Mueen;Eamonn Keogh;Neal Young

  • Curse of Dimensionality.

    Eamonn J. Keogh;Abdullah Mueen

  • DeBot: Twitter Bot Detection via Warped Correlation

    Nikan Chavoshi;Hossein Hamooni;Abdullah Mueen

  • Addressing Big Data Time Series: Mining Trillions of Time Series Subsequences Under Dynamic Time Warping

    Thanawin Rakthanmanon;Bilson Campana;Abdullah Mueen;Gustavo Batista

  • Clustering Time Series Using Unsupervised-Shapelets

    Jesin Zakaria;Abdullah Mueen;Eamonn Keogh

  • Matrix Profile II: Exploiting a Novel Algorithm and GPUs to Break the One Hundred Million Barrier for Time Series Motifs and Joins

    Yan Zhu;Zachary Zimmerman;Nader Shakibay Senobari;Chin-Chia Michael Yeh

  • LogMine: Fast Pattern Recognition for Log Analytics

    Hossein Hamooni;Biplob Debnath;Jianwu Xu;Hui Zhang

  • Online discovery and maintenance of time series motifs

    Abdullah Mueen;Eamonn Keogh

  • Extracting Optimal Performance from Dynamic Time Warping

    Abdullah Mueen;Eamonn Keogh

  • Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs

    Doruk Sart;Abdullah Mueen;Walid Najjar;Eamonn Keogh

  • Fast approximate correlation for massive time-series data

    Abdullah Mueen;Suman Nath;Jie Liu

  • Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile

    Chin-Chia Michael Yeh;Yan Zhu;Liudmila Ulanova;Nurjahan Begum

  • Enumeration of time series motifs of all lengths

    Abdullah Mueen;Nikan Chavoshi

  • Analyzing the Great Firewall of China Over Space and Time

    Roya Ensafi;Philipp Winter;Abdullah Mueen;Jedidiah R. Crandall

  • Enumeration of Time Series Motifs of All Lengths

    Abdullah Mueen

  • Identifying Correlated Bots in Twitter

    Nikan Chavoshi;Hossein Hamooni;Abdullah Mueen

Frequent Co-Authors

Eamonn Keogh
Eamonn Keogh University of California, Riverside
Gustavo E. A. P. A. Batista
Gustavo E. A. P. A. Batista University of New South Wales
Michalis Faloutsos
Michalis Faloutsos University of California, Riverside
Neal E. Young
Neal E. Young University of California, Riverside
James F. Cavanagh
James F. Cavanagh University of New Mexico
Vassilis J. Tsotras
Vassilis J. Tsotras University of California, Riverside
M. Brandon Westover
M. Brandon Westover Harvard University
Sydney S. Cash
Sydney S. Cash Harvard University
Alexander V. Balatsky
Alexander V. Balatsky University of Connecticut
Philip Brisk
Philip Brisk University of California, Riverside

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