STGI：a spatio-temporal grid index model for marine big data. Issue 4 (1st October 2020)

• Record Type: Journal Article
• Authors: Qu, Tengteng; Wang, Lizhe; Yu, Jian; Yan, Jining; Xu, Guilin; Li, Meng; Cheng, Chengqi; Hou, Kaihua; Chen, Bo
• Is Part Of: Big earth data. Volume 4:Issue 4(2020)
• Page Start: 435
• Page End: 450
• Publication Date: 2020-10-01
• Subjects: GeoSOT -- spatio-temporal grid index model -- marine big data -- MongoDB; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2020.1844933
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Marine big data are characterized by a large amount and complex structures, which bring great challenges to data management and retrieval. Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database, this paper proposes a spatio-temporal grid index model (STGI) for efficient optimized query of marine big data. A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data. Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models. Through theoretical analysis and experimental verification, the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency, such as marine big data.