MapReduce for Large-Scale Monitor Data Analyses

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.