The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans les scénarios d'intelligence artificielle pour les opérations informatiques, les KPI (Key Performance Indicator) sont un indicateur très important de surveillance des opérations et de la maintenance, et la recherche sur la détection des anomalies KPI est également devenue un point chaud ces dernières années. Visant les problèmes de faible efficacité de détection et d'apprentissage de représentation insuffisant des méthodes existantes, cet article propose une méthode de détection d'anomalies KPI rapide basée sur le clustering, HCE-DWL. Cet article adopte d'abord la combinaison du clustering agglomératif hiérarchique (HAC) et de l'affectation approfondie basée sur CNN-Embedding (CE) pour effectuer une analyse de cluster (c'est-à-dire HCE) sur les données KPI, afin d'améliorer l'efficacité du clustering des données KPI, puis séparément, le centroïde de chaque cluster KPI et ses scores aberrants transformés (TOS) reçoivent des poids, et enfin ils sont placés dans le modèle LightGBM pour détection (le modèle Double Weight LightGBM, appelé DWL). Grâce à une analyse expérimentale comparative, il est prouvé que l'algorithme peut améliorer efficacement l'efficacité et la précision de la détection des anomalies KPI.
Yun WU
Northeast Electric Power University
Yu SHI
Northeast Electric Power University
Jieming YANG
Northeast Electric Power University
Lishan BAO
Industry Group Co. Ltd.
Chunzhe LI
State Grid Jilin Electric Power Co. Ltd.
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Yun WU, Yu SHI, Jieming YANG, Lishan BAO, Chunzhe LI, "A KPI Anomaly Detection Method Based on Fast Clustering" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 11, pp. 1309-1317, November 2022, doi: 10.1587/transcom.2021TMP0002.
Abstract: In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021TMP0002/_p
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@ARTICLE{e105-b_11_1309,
author={Yun WU, Yu SHI, Jieming YANG, Lishan BAO, Chunzhe LI, },
journal={IEICE TRANSACTIONS on Communications},
title={A KPI Anomaly Detection Method Based on Fast Clustering},
year={2022},
volume={E105-B},
number={11},
pages={1309-1317},
abstract={In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.},
keywords={},
doi={10.1587/transcom.2021TMP0002},
ISSN={1745-1345},
month={November},}
Copier
TY - JOUR
TI - A KPI Anomaly Detection Method Based on Fast Clustering
T2 - IEICE TRANSACTIONS on Communications
SP - 1309
EP - 1317
AU - Yun WU
AU - Yu SHI
AU - Jieming YANG
AU - Lishan BAO
AU - Chunzhe LI
PY - 2022
DO - 10.1587/transcom.2021TMP0002
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2022
AB - In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.
ER -