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

Abstract

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.