An Integrated Framework for Analysis and Mining of the Massive Sensor Data Using Feature Preserving Strategy on Cloud Computing

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NETL
IEEE

外文摘要：Cloud computing can provide a powerful， scalable storage and the massive data processing infrastructure to perform both online and offline analysis and mining of the heterogeneous sensor data streams。 In contrast to traditional data objects， the sensor data objects from the Internet of Thing (IoT) monitoring application have continuously changing， high-dimensional， spatiotemporal relation and heterogeneous attributes。 Therefore， the analysis and mining problem of the massive sensor data objects can be more complicated。 The paper formally presents an integrated framework for analysis problem of the massive sensor data with insights into the high-dimensional problem using the feature preserving on cloud computing。 The proposed framework realized the cloud resources independent dynamic allocation and scheduling for the massive sensor data mining using kernel methods for reducing the computation of spatial data retrieval。 As the experiment results shown， the strategy can preserve important spatial feature information and provide effective preprocessing analysis results。

外文关键词：

Cloud computingFeature preservingMassive sensor data streams managementParallel processing

作者：

Xin Song、Cuirong Wang、Jing Gao

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作者单位：

Northeastern Univ. at Qinhuangdao, Qinhuangdao, China

会议名称：

International Symposium on Computational Intelligence and Design

会议地点：

Hangzhou(CN)

会议母体文献：

2014 Seventh International Symposium on Computational Intelligence and Design

页码：

337-340

出版时间：

2014

DOI：

10.1109/ISCID.2014.278