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A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
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School of Computer and Remote Sensing Information Technology, North China Institute of Aerospace Engineering, Langfang 065000, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 533; https://doi.org/10.3390/ijgi8120533
Received: 16 September 2019 / Revised: 11 November 2019 / Accepted: 24 November 2019 / Published: 27 November 2019
With the rapid development of earth-observation technology, the amount of remote sensing data has increased exponentially, and traditional relational databases cannot satisfy the requirements of managing large-scale remote sensing data. To address this problem, this paper undertakes intensive research of the NoSQL (Not Only SQL) data management model, especially the MongoDB database, and proposes a new approach to managing large-scale remote sensing data. Firstly, based on the sharding technology of MongoDB, a distributed cluster architecture was designed and established for massive remote sensing data. Secondly, for the convenience in the unified management of remote sensing data, an archiving model was constructed, and remote sensing data, including structured metadata and unstructured image data, were stored in the above cluster separately, with the metadata stored in the form of a document, and image data stored with the GridFS mechanism. Finally, by designing different shard strategies and comparing MongoDB cluster with a typical relational database, several groups of experiments were conducted to verify the storage performance and access performance of the cluster. The experimental results show that the proposed method can overcome the deficiencies of traditional methods, as well as scale out the database, which is more suitable for managing massive remote sensing data and can provide technical support for the management of massive remote sensing data. View Full-Text
Keywords: remote sensing data; MongoDB; data management; sharding technology; GridFS mechanism remote sensing data; MongoDB; data management; sharding technology; GridFS mechanism
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Wang, S.; Li, G.; Yao, X.; Zeng, Y.; Pang, L.; Zhang, L. A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB. ISPRS Int. J. Geo-Inf. 2019, 8, 533.

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