This book constitutes the thoroughly refereed post-competition proceedings of the AI Ops Competition on Large-Scale Disk Failure Prediction, conducted between February 7th and May 15, 2020 on the Alibaba Cloud Tianchi Platform. A dedicated workshop, featuring the best performing teams of the competition, was held at the 24
th
Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, in Singapore, in April 2019. Due to the COVID-19 pandemic, the workshop was hosted online.
This book includes 13 selected contributions: an introduction to dataset, selected approaches of the competing teams and the competition summary, describing the competition task, practical challenges, evaluation metrics, etc.
Series
Edition
Language
Place of publication
Target group
Professional and scholarly
Illustrations
40 farbige Abbildungen, 56 s/w Abbildungen
X, 143 p. 96 illus., 40 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 9 mm
Weight
ISBN-13
978-981-15-7748-2 (9789811577482)
DOI
10.1007/978-981-15-7749-9
Schweitzer Classification
An Introduction to PAKDD CUP 2020 Dataset.- PAKDD 2020 Alibaba AIOps Competition-Large-scale Disk Failure Prediction: Third Place Team.- A Voting-based Robust Model for Disk Failure Prediction.- First place solution of PAKDD Cup 2020.- Anomaly Detection of Hard Disk Drives based on Multi-scale Feature.- Disk Failure Prediction: an in-depth comparison between deep neural networks and tree-based models.- PAKDD2020 Alibaba AI Ops Competition: Large-Scale Disk Failure Prediction.- SHARP: SMART HDD Anomaly Risk Prediction.- Tree-based model with advanced data preprocessing for large scale hard disk failure prediction.- PAKDD2020 Alibaba AI Ops Competition: An SPE-LightGBM Approach.- Noise Feature Selection Method in PAKDD 2020 Alibaba AI Ops Competition: Large-Scale Disk Failure Prediction.- Characterizing and Modeling for Proactive Disk Failure Prediction to Improve Reliability of Data Centers.- Summary of PAKDD CUP 2020: From Organizers' Perspective.
