SQL versus NoSQL databases for geospatial applications

Baralis, Elena; Valle, Andrea Dalla; Garza, Paolo; Rossi, Claudio; Scullino, Francesco

doi:10.1109/bigdata.2017.8258324

Cited by 27 publications

(16 citation statements)

References 12 publications

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“…The flexibility allowed here could be useful when building a geoservice to fetch data or compute any other spatial operation, knowing that MongoDB performs better at simple operations (read, insert) or common spatial operations like line intersection and point containment [5]. These types of request can be served from MongoDB directly, while complex operations like spatial joins with filtering or geometry subdivisions can be addressed from the PostgreSQL side.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, relational databases are efficient when handling large amounts of data that have a fixed structure and guarantee ACID (atomicity, consistency, isolation, durability) properties in transactions [4]. Some of them also have better geospatial support than most NoSQL engines, providing more complex spatial operations and indexes; they are more easily compatible with other geospatial tools like Mapserver and QGIS [5].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid storage engine for geospatial data using NoSQL and SQL paradigms

Herrera-Ramírez¹,

Treviño-Villalobos²,

Víquez-Acuña³

2021

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The design and implementation of services to handle geospatial data involves thinking about storage engine performance and optimization for the desired use. NoSQL and relational databases bring their own advantages; therefore, it is necessary to choose one of these options according to the requirements of the solution. These requirements can change, or some operations may be performed in a more efficient way on another database engine, so using just one engine means being tied to its features and work model. This paper presents a hybrid approach (NoSQL-SQL) to store geospatial data on MongoDB, which are replicated and mapped on a PostgreSQL database, using an open source tool called ToroDB Stampede; solutions then can take advantage from either NoSQL or SQL features, to satisfy most of the requirements associated to the storage engine performance. A descriptive analysis to explain the workflow of the replication and synchronization in both engines precedes the quantitative analysis by which it was possible to determine that a normal database in PostgreSQL has a shorter response time than to perform the query in PostgreSQL with the hybrid database. In addition, the type of geometry increases the update response time of a materialized view.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid storage engine for geospatial data using NoSQL and SQL paradigms

Herrera-Ramírez¹,

Treviño-Villalobos²,

Víquez-Acuña³

2021

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“…The performance of NoSQL databases is evaluated and discussed by several researchers for spatial workloads [133,135]. Some of these performance analyses also involve comparisons with relational spatial databases [30,31,131,159]. Hadoop [16] is a highly scalable and distributed MapReduce [68] framework for processing big data.…”

Section: Spatial Nosql Databasesmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

2022

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Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

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“…Compared to relational databases, their capabilities are still much smaller. Additionally, our initial requirement was that project results will be used in a production system, so greater maturity and versatility at the database level were required which, according to Baralis, Dalla Valle, Garza, and Scullino (), NoSQL databases in general do not yet provide. Because there is no database solving all the problems, and in particular because, according to Khine and Wang (), graph databases work well only when it is necessary to manage very complex relationships, we decided to choose an approach referred to as “polyglot persistence,” that is, to use multiple data storage technologies to handle different application needs, to have the possibility of using a multi‐model data store architecture in the future.…”

Section: Related Workmentioning

confidence: 99%

Modeling and presenting incomplete and uncertain data on historical settlement units

Myrda

Szady

Ławrynowicz

2020

Transactions in GIS

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When entering information about changes of settlements in time into a database, we are dealing with direct facts from historical sources and with interpretations. The source information generally relates to an event or situation at a specific time. This means that we only fill in the parts of the timeline with data objects called (partial) manifestations; however, lack of data does not mean that settlement did not exist. It would take a lot of time to interpret and enter missing information manually. In this article we present a database schema, database procedures and algorithms to generate aggregated settlement unit manifestations in an automated way from their partial manifestations. As a result, it is possible to obtain a continuous history of a given settlement so as to present it on a map.

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SQL versus NoSQL databases for geospatial applications

Cited by 27 publications

References 12 publications

Hybrid storage engine for geospatial data using NoSQL and SQL paradigms

Hybrid storage engine for geospatial data using NoSQL and SQL paradigms

A Survey on Spatio-temporal Data Analytics Systems

Modeling and presenting incomplete and uncertain data on historical settlement units

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