A Massive Sensor Data Streams Multi-dimensional Analysis Strategy Using Progressive Logarithmic Tilted Time Frame for Cloud-Based Monitoring Application

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8866)

Abstract

The massive sensor data streams multi-dimensional analysis in the monitoring application of internet of things is very important, especially in the environments where supporting such kind of real time streaming data storage and management. 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 order to support high-volume and real-time sensor data streams processing, in this paper, we propose a massive sensor data streams multi-dimensional analysis strategy using progressive logarithmic tilted time frame for cloud based monitoring application. The proposed strategy is sufficient for many high-dimensional streams analysis tasks using map-reduce platform of cloud computing. Finally, the simulation results show that proposed strategy achieves the enhancing storage performance and also can ensures that the total amount of data to retain in memory or to be stored on disk is small for achieving the performance improvement of the massive sensor data streams analysis.

Keywords

Massive data streams analysis Multi-dimensional streams data processing Progressive logarithmic tilted time frame Cloud computing 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Northeastern University at QinhuangdaoQinhuangdaoChina

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