State-of-the-Art Geospatial Information Processing in NoSQL Databases
Bibliographic Data
| ID | 22034191 |
|---|---|
| Authors | Dongming Guo (0000-0001-5357-8410, Norwegian University of Science and Technology, corresponding author), Erling Onstein (0000-0002-5447-7563, Norwegian University of Science and Technology) |
| Year | 2020 |
| Volume | 9 |
| Issue | 5 |
| Pages | 331 |
| Publication date | 2020-05-19 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9050331 |
| OpenAlex | W3027702097 |
| Language | EN |
| Citations received | 1 |
| References cited | 65 |
Geospatial information has been indispensable for many application fields, including traffic planning, urban planning, and energy management. Geospatial data are mainly stored in relational databases that have been developed over several decades, and most geographic information applications are desktop applications. With the arrival of big data, geospatial information applications are also being modified into, e.g., mobile platforms and Geospatial Web Services, which require changeable data schemas, faster query response times, and more flexible scalability than traditional spatial relational databases currently have. To respond to these new requirements, NoSQL (Not only SQL) databases are now being adopted for geospatial data storage, management, and queries. This paper reviews state-of-the-art geospatial data processing in the 10 most popular NoSQL databases. We summarize the supported geometry objects, main geometry functions, spatial indexes, query languages, and data formats of these 10 NoSQL databases. Moreover, the pros and cons of these NoSQL databases are analyzed in terms of geospatial data processing. A literature review and analysis showed that current document databases may be more suitable for massive geospatial data processing than are other NoSQL databases due to their comprehensive support for geometry objects and data formats and their performance, geospatial functions, index methods, and academic development. However, depending on the application scenarios, graph databases, key-value, and wide column databases have their own advantages
Big data · Data mining · Database · Geography · Geospatial analysis · Geospatial PDF · Information retrieval · NoSQL · Relational database · Remote sensing · Scalability · Spatial analysis · Spatial database · SQL · Computer Science · Constraint Satisfaction and Optimization · Data Management and Algorithms · Geographic Information Systems Studies
| Citations per year | 0.2 |
|---|---|
| Citation span | 2021 - 2021 (1) |
| Highly cited | No |