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KPIs Inventory

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AutoKAD: Empowering KPI Anomaly Detection with Label-Free Deployment

IEEE International Symposium on Software Reliability Engineering, 2023
Monitoring Key Performance Indicators (KPIs) and detecting anomalies in online service systems is critical. However, choosing the right KPI anomaly detection algorithm and appropriate hyperparameters presents a challenge.
Zhaoyang Yu, Changhua Pei, Shenglin Zhang, Xidao Wen, Jianhui Li, Gaogang Xie, Dan Pei   +6 more
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Artificial intelligence to counteract "KPI overload" in business process monitoring: the case of anti-corruption in public organizations

Business Process Management Journal, 2023
PurposeThe nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the ...
Simone Caruso, M. Bruccoleri, A. Pietrosi, A. Scaccianoce   +3 more
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Learning Spatial Graph Structure for Multivariate KPI Anomaly Detection in Large-Scale Cyber-Physical Systems

IEEE Transactions on Instrumentation and Measurement, 2023
Anomaly detection on multivariate key performance indicators (KPIs) is a key procedure for the quality and reliability of large-scale cyber-physical systems (CPSs).
Haiqi Zhu, Seung-Ryong Rho, Shaohui Liu, Feng Jiang   +3 more
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Efficient KPI Anomaly Detection Through Transfer Learning for Large-Scale Web Services

IEEE Journal on Selected Areas in Communications, 2022
Timely anomaly detection of key performance indicators (KPIs), e.g., service response time, error rate, is of utmost importance to Web services. Over the years, many unsupervised deep learning-based anomaly detection approaches have been proposed.
Shenglin Zhang, Zhenyu Zhong, Dongwen Li, Qiliang Fan, Yongqian Sun, Man Zhu, Yuzhi Zhang, Dan Pei, Jiyan Sun, Yinlong Liu, Hui Yang, Yongqiang Zou   +11 more
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A Stacking Learning Data-Driven Method for Nonlinear KPI-Related Fault Detection

IEEE Transactions on Circuits and Systems - II - Express Briefs, 2023
This brief studies the key performance indicator (KPI)-related fault detection problem for the nonlinear system based on the designed stacking learning data-driven (SLDD) method.
Chengyuan Sun, Guang‐Hong Yang
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Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data

IEEE Transactions on Industrial Informatics, 2021
Soft-sensing techniques are of great significance in industrial processes for monitoring and prediction of key performance indicators. Due to the effectiveness of nonlinear feature extraction and strong expansibility, an autoencoder (AE) and its ...
Qingqiang Sun, Zhiqiang Ge
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ML KPI Prediction in 5G and B5G Networks

2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2023
Network operators are facing new challenges when meeting the needs of their customers. The challenges arise due to the rise of new services, such as HD video streaming, IoT, autonomous driving, etc., and the exponential growth of network traffic. In this
Nguyen Phuc Tran, Oscar Delgado, B. Jaumard, Fadi Bishay   +3 more
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Demonstrating Network Slice KPI Monitoring in a 5G Testbed

IEEE/IFIP Network Operations and Management Symposium, 2022
Network slicing has been envisaged as a key enabler to satisfy diverse requirements of 5G networks, by creating multiple isolated end-to-end virtual networks dedicated to different services.
Niloy Saha, Alexander James, Nashid Shahriar, R. Boutaba, A. Saleh   +4 more
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TSAGen: Synthetic Time Series Generation for KPI Anomaly Detection

IEEE Transactions on Network and Service Management, 2021
A key performance indicator (KPI) consists of critical time series data that reflect the runtime states of network systems (e.g., response time and available bandwidth).
Chengyu Wang, Kui Wu, Tongqing Zhou, Guang Yu, Zhiping Cai   +4 more
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