Research of Elevator Security Data Mining Based on Hadoop

被引：0

作者：

Wang, Guicheng ^{[1
]}

Han, Changxi ^{[1
]}

Zhang, Chunming ^{[2
]}

Xu, Ye ^{[1
]}

Zhang, Min ^{[1
]}

机构：

[1] Shanghai Inst Technol, Shanghai 201418, Peoples R China

[2] Engn Univ CAPF, Informat Engn Dept, Xian 710016, Peoples R China

来源：

PROCEEDINGS OF THE 2019 31ST CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2019) | 2019年

关键词：

Elevator safety; Data mining; Hadoop platform; K-Means algorithm; Apriori algorithm;

D O I：

10.1109/ccdc.2019.8833076

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hadoop data platform is studied to mine the massive elevator data from the elevator remote supervision system. The data mining and analysis platform is designed by Hadoop minimum cluster theory, which is mainly four modules composed of data transmission, data preprocessing, data mining and scheduling. Then both K-Means which is a cluster analysis algorithm and Apriori which is an association rule algorithm are improved, and parallelized with the computation framework of Hadoop's MapReduce. Finally, all modules are integrated through the scheduling module, and the practicability of the data mining and analysis platform is verified and tested, and the data mining of security status is realized for the elevator.

引用

页码：4441 / 4444

页数：4

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