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Large-scale disk failure prediction ...

Pacific-Asia Conference on Knowledge Discovery and Data Mining (2020 :)

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Large-scale disk failure prediction = PAKDD 2020 Competition and Workshop, AI Ops 2020, February 7 - May 15, 2020 : revised selected papers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large-scale disk failure prediction/ edited by Cheng He ... [et al.].
其他題名:	PAKDD 2020 Competition and Workshop, AI Ops 2020, February 7 - May 15, 2020 : revised selected papers /
其他題名:	AI Ops 2020
其他作者:	He, Cheng.
團體作者:	Pacific-Asia Conference on Knowledge Discovery and Data Mining
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	x, 143 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Data mining - Congresses. -
電子資源:	https://doi.org/10.1007/978-981-15-7749-9
ISBN:	9789811577499

Large-scale disk failure prediction = PAKDD 2020 Competition and Workshop, AI Ops 2020, February 7 - May 15, 2020 : revised selected papers /
Large-scale disk failure predictionPAKDD 2020 Competition and Workshop, AI Ops 2020, February 7 - May 15, 2020 : revised selected papers /[electronic resource] :AI Ops 2020edited by Cheng He ... [et al.]. - Singapore :Springer Singapore :2020. - x, 143 p. :ill., digital ;24 cm. - Communications in computer and information science,12611865-0929 ;. - Communications in computer and information science ;1261..

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.

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 24th 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.

ISBN: 9789811577499

Standard No.: 10.1007/978-981-15-7749-9doiSubjects--Topical Terms:

551626
Data mining
--Congresses.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

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