Spatial Data and Graph Databases for Identifying Relations Among Members of Cattle Herd

Mueller, W.

doi:10.5593/sgem2016/b21/s08.105

Cited by 1 publication

(2 citation statements)

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“…Using a carpool matching strategy, the efficiency and efficacy of the proposed system were evaluated [53]. Neo4j has also been applied in agriculture and animal husbandry applications, for example, using web technology and a Neo4j shell to evaluate the condition of the crops on the basis of geospatial data [54] and identifying relations between the members of a cattle herd based on spatial and graph databases [55].…”

Section: Neo4jmentioning

confidence: 99%

“…However, because of the characteristics of these two data models, Neo4j and Redis mainly support the point geometry object. Therefore, key-value databases (Redis) [7,91] and graph databases (Neo4j) [53][54][55] only provide limited geospatial queries and functions, including distance calculations and location queries. Restricted geospatial data models hinder the applications for which key-value databases (Redis) and graph databases (Neo4j) are useful due to the complexity of geospatial data, especially that of polyline and polygon objects.…”

Section: Comparisons Of Geospatial Data Processing In Nosql Databasesmentioning

confidence: 99%

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State-of-the-Art Geospatial Information Processing in NoSQL Databases

Guo

Onstein

2020

IJGI

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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.

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Section: Neo4jmentioning

confidence: 99%