An efficient index for massive IOT data in cloud environment

Authors:
Youzhong Ma;Jia Rao;Weisong Hu;Xiaofeng Meng;Xu Han;Yu Zhang;Yunpeng Chai;Chunqiu Liu
Affiliations:
Renmin University of China, Beijing & Zhengzhou Chenggong University of Finance and Economics, Henan, China;NEC Labs, China, Beijing, China;NEC Labs, China, Beijing, China;Renmin University of China, Beijing, China;Renmin University of China, Beijing, China;Renmin University of China, Beijing, China;Renmin University of China, Beijing, China;Renmin University of China, Beijing, China
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

The Internet of Things (IOT) has been widely applied in many fields, while the IOT data are always large volume, update frequently and inherently multi-dimensional, these characteristics bring big challenges to the traditional DBMSs. The traditional DBMSs have rich functionality and can deal with multi-attributes access efficiently, they can not scale good enough to deal with large volume data and can not support high insert throughput. The cloud-based database systems have good scalability, but they don't support multi-dimensional access natively.In order to deal with the large volume of IOT data, we propose an update and query efficient index framework (UQE-Index) based on key-value store that can support high insert throughput and provide efficient multi-dimensional query simultaneously. We implemented a prototype based on HBase and did comprehensive experiments to test our solution's scalability and efficiency.