Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging...

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Bibliographic Details
Published in:China communications 2017-11, Vol.14 (11), p.39-47
Main Authors: Wang, Hongman, Zeng, Mengqi, Xiong, Zijie, Yang, Fangchun
Format: Article
Language:English
Subjects:
Accidents
Algorithm design and analysis
apriori
Collaboration
data cube
Data mining
Data models
edge computing
elevator group accidents
Elevators
Itemsets
multi-dimensional association rules
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Summary:In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.
ISSN:1673-5447
DOI:10.1109/CC.2017.8233649