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Cited by 100Anomaly Detection Techniques and ApplicationsSoftware System Performance and ReliabilityCloud Computing and Resource Management

Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach

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Song, Yujia, Xin, Ruyue, Chen, Peng, Zhang, Rui, Chen, Juan, Zhao, Zhiming·2023·Future Generation Computer Systems
DOI 10.1016/j.future.2023.03.020
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  • By using sparse and variational autoencoder architectures, TVGeAN can capture intrinsic knowledge from a small training space and generalize it effectively without using complex approaches.

    “Furthermore, by using sparse and variational autoencoder architectures, TVGeAN can capture the intrinsic knowledge from a small training space and generalize it effectively without using complex approaches, as is the case with the models that use the dynamic graph approach, e.g., refs.[103-106].”

    From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [103]

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Research works printed but not linked: 19

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Authors
Song, Y., Xin, R., Chen, P., Zhang, R., Chen, J., & Zhao, Z.
Affiliations
  • Xihua University (CN)
  • University of Amsterdam (NL)
Corresponding author
  • Chen, Peng
  • Zhao, Zhiming
ORCID
Venue
Future Generation Computer Systems
Publisher
Elsevier BV
Published
2023-08
Volume
145
Pages
77-86
ISSN
0167-739X (ISSN-L)
DOI
10.1016/j.future.2023.03.020
OpenAlex
W4327718158
Cited by
100
References
2
Citations by year
2026: 13 · 2025: 31 · 2024: 20 · 2023: 36
FWCI
8.85
Type
journal article
Language
en
Open access
Licence
Locations
  • Future Generation Computer Systems (publishedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
  • UvA-DARE (University of Amsterdam) (submittedVersion)
Funders
  • European Commission (824068, HORIZON2020, 862409, 825134)
  • China Scholarship Council
  • LifeWatch – Niclas Öberg Foundation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
multivariate time series anomaly detection, graph neural networks, multi-head self-attention, explainable anomaly detection, root cause analysis, false positive reduction

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