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| a relational database and key-value store combined mechanism for massive heterogeneous sensor data management | |
| Ding Zhiming; Yang Qi; Guo Limin | |
| 2012 | |
| Conference Name | 1st International Conference on Sensor Networks, SENSORNETS 2012 |
| Source | SENSORNETS 2012 - Proceedings of the 1st International Conference on Sensor Networks |
| Pages | 151-154 |
| Conference Date | February 24, 2012 - February 26, 2012 |
| Conference Place | Rome, Italy |
| Indexed Type | EI |
| ISBN | 9789898565013 |
| Department | (1) Institute of Software Chinese Academy of Sciences South Fourth Str. 4 Zhongguancun Beijing 100190 China; (2) National Center ITS Engineering and Technology Xitucheng Road 8 Beijing 100088 China |
| English Abstract | Massive sensor data management is an important issue in large-scale sensor based systems such as the Internet/web of Things. However, existing relational database and cloud data management techniques are inadequate in handling large-scale sensor sampling data. On the one hand, relational databases can not efficiently process frequent data updates caused by sensor samplings. On the other hand, current cloud data management mechanisms are largely key-value stores so that they can not support complicated spatialtemporal computation involved in sensor data query. To solve the above problems, we propose a Relational Data-Base and Key-Value store combined Cloud Data management ("RDB-KV CloudDB") framework, in this paper. The experimental results show that the RDB-KV CloudDB can provide satisfactory query processing and sensor data updating performances in large scale sensor-based systems.; Massive sensor data management is an important issue in large-scale sensor based systems such as the Internet/web of Things. However, existing relational database and cloud data management techniques are inadequate in handling large-scale sensor sampling data. On the one hand, relational databases can not efficiently process frequent data updates caused by sensor samplings. On the other hand, current cloud data management mechanisms are largely key-value stores so that they can not support complicated spatialtemporal computation involved in sensor data query. To solve the above problems, we propose a Relational Data-Base and Key-Value store combined Cloud Data management ("RDB-KV CloudDB") framework, in this paper. The experimental results show that the RDB-KV CloudDB can provide satisfactory query processing and sensor data updating performances in large scale sensor-based systems. |
| Keyword | Data Processing Database Systems Sensor Networks |
| Sponsorship | Inst. Syst. Technol. Inf., Control Commun. (INSTICC) |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15691 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Ding Zhiming,Yang Qi,Guo Limin. a relational database and key-value store combined mechanism for massive heterogeneous sensor data management[C],2012:151-154. |
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