DocumentCode :
1799816
Title :
MapReduce for Large-Scale Monitor Data Analyses
Author :
Jianwei Ding ; Yingbo Liu ; Li Zhang ; Jianmin Wang
Author_Institution :
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China
fYear :
2014
fDate :
24-26 Sept. 2014
Firstpage :
747
Lastpage :
754
Abstract :
The recent years witness the rapid development of the Internet of Things (IoT). Increasing numbers of conventional manufacturing enterprises face the challenge of using current data management systems to collect and analyze the massive volume of monitor data generated by sensors and equipments to improve the design, manufacture and maintenance of products. Hence, Map Reduce technique has gained a lot of attention for its applicability in large parallel monitor data analyses. In this paper, we apply a series of metrics implemented based on Map Reduce framework to depict a large collection of monitor data. With the help of these proposed metrics, domain experts can quickly analyze monitor data and feedback the analysis results to improve the design, manufacture and maintenance of products. Furthermore, we conduct a series of experiments on the real-world data sets and experimental results show that when facing a large amount of monitor data, these proposed Map Reduce implemented metrics outperform the conventional structured databases implemented metrics. Moreover, these Map Reduce implemented metrics have been successfully applied to a construction machinery manufacturer´s condition monitoring system.
Keywords :
Internet of Things; condition monitoring; construction equipment; data analysis; database management systems; machinery production industries; maintenance engineering; mechanical engineering computing; parallel processing; product design; Internet of Things; IoT; MapReduce; construction machinery manufacturer condition monitoring system; data management systems; large-scale monitor data analysis; manufacturing enterprises; parallel monitor data analysis; product design; product maintenance; product manufacturing; structured databases; Distributed databases; Monitoring; Temperature measurement; Temperature sensors; Time series analysis; MapReduce; Metric; Monitor Data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Trust, Security and Privacy in Computing and Communications (TrustCom), 2014 IEEE 13th International Conference on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/TrustCom.2014.98
Filename :
7011322
Link To Document :
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