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Title:
a relational database and key-value store combined mechanism for massive heterogeneous sensor data management
Author: Ding Zhiming ; Yang Qi ; Guo Limin
Source: SENSORNETS 2012 - Proceedings of the 1st International Conference on Sensor Networks
Conference Name: 1st International Conference on Sensor Networks, SENSORNETS 2012
Conference Date: February 24, 2012 - February 26, 2012
Issued Date: 2012
Conference Place: Rome, Italy
Keyword: Data processing ; Database systems ; Sensor networks
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
Sponsorship: Inst. Syst. Technol. Inf., Control Commun. (INSTICC)
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15691
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Ding Zhiming,Yang Qi,Guo Limin. a relational database and key-value store combined mechanism for massive heterogeneous sensor data management[C]. 见:1st International Conference on Sensor Networks, SENSORNETS 2012. Rome, Italy. February 24, 2012 - February 26, 2012.
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