DocumentCode :
267102
Title :
Predictive Analytics of Sensor Data Using Distributed Machine Learning Techniques
Author :
Kejela, Girma ; Esteves, Rui Maximo ; Chunming Rong
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Stavanger, Stavanger, Norway
fYear :
2014
fDate :
15-18 Dec. 2014
Firstpage :
626
Lastpage :
631
Abstract :
This work is based on a real-life data-set collected from sensors that monitor drilling processes and equipment in an oil and gas company. The sensor data stream-in at an interval of one second, which is equivalent to 86400 rows of data per day. After studying state-of-the-art Big Data analytics tools including Mahout, RHadoop and Spark, we chose Ox data´s H2O for this particular problem because of its fast in-memory processing, strong machine learning engine, and ease of use. Accurate predictive analytics of big sensor data can be used to estimate missed values, or to replace incorrect readings due malfunctioning sensors or broken communication channel. It can also be used to anticipate situations that help in various decision makings, including maintenance planning and operation.
Keywords :
Big Data; data analysis; learning (artificial intelligence); Big Data analytics tools; Mahout; RHadoop; Spark; big sensor data; distributed machine learning techniques; drilling process monitoring; inmemory processing; maintenance planning; predictive analytics; Analytical models; Big data; Computational modeling; Data models; Machine learning algorithms; Predictive models; Water; Big Data; GBM; GLM; Generalized Linear Model; Gradient Boosted Model; H2O; Machine Learning; Predictive Analytics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/CloudCom.2014.44
Filename :
7037726
Link To Document :
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