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

Guo, Dongming; Onstein, Erling

doi:10.3390/ijgi9050331

Cited by 35 publications

(23 citation statements)

References 84 publications

(178 reference statements)

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“…Amazon DynamoDB is a document-based key-value database, so it brings an approach to database modelling different from traditional relational databases [39]. Amazon DynamoDB was used as a database component to store all entities.…”

Section: Used Services In Amazon Web Servicementioning

confidence: 99%

Serverless Geospatial Data Processing Workflow System Design

Pakdil

Çeli̇k

2021

IJGI

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Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.

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Section: Used Services In Amazon Web Servicementioning

confidence: 99%

Serverless Geospatial Data Processing Workflow System Design

Pakdil

Çeli̇k

2021

IJGI

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“…• more flexible scalability, also in comparison with the relational model [6]. At the same time, the disadvantages of NoSQL DBMS when working with spatial data are also present and among the most significant it should be noted:…”

Section: Overviewmentioning

confidence: 99%

Using a DBMS based on various data models in solving problems of cartography and geoinformatics

Kolesnikov

Кикин

2021

E3S Web Conf.

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An increasing number of database management systems are expanding their functionality to work with various types of spatial data. This is true for both relational and NoSQL data models. The article describes the main features of those data models for which the functions of storing and processing spatial data are implemented. A comparative analysis of the performance of typical spatial queries for database management systems based on various data models, including multi-model ones, is carried out. The dataset on which the comparison is performed is presented in the form of three blocks of OpenStreetMap vector data for the territory of the Novosibirsk region. Based on the results of the study, recommendations are made on the use of certain data models, depending on the available data and the tasks to be solved.

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“…The big trajectory systems include a cloud-based system on Microsoft Azure [21], ST-Hadoop [9], TrajSpark [272], DiStRDF [164], and systems based on Apache Flink, MongoDB [122], and other databases for semantic trajectories. Recently, Guo et al [108] have surveyed the geospatial data processing capabilities in the 10 most popular NoSQL databases based on supported geometry types, geometry functions, spatial indexes, query languages, and data formats. This survey also discussed the strengths and weaknesses of each of these databases.…”

Section: Related Workmentioning

confidence: 99%

“…However, in the meantime, a number of big data processing systems for spatio-temporal and trajectory data emerged in addition to a few new big spatial data processing systems. Though Guo et al [108] have reviewed native spatial supports of NoSQL databases, there is no comprehensive survey of big spatial data processing systems, which are developed by utilizing NoSQL databases. Moreover, there is no review available on Python libraries such as DASK and RAPIDS for big spatial data processing.…”

Section: Related Workmentioning

confidence: 99%

A Survey on Spatio-temporal Data Analytics Systems

Alam¹,

Torgo²,

Bifet³

2021

Preprint

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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 spatiotemporal 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 for processing spatio-temporal data, or by implementing algorithms for mining spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics can be categorized into three groups, (1) spatial databases (SQL and NoSQL), ( 2) big spatio-temporal data processing infrastructures, and (3) programming languages and software tools for processing spatio-temporal data. Since existing surveys mostly investigated big data infrastructures for processing spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics along with an up-to-date review of big spatial data processing systems. This survey also portrays the importance and future of spatial and spatio-temporal data analytics. CCS Concepts: • General and reference → Surveys and overviews; • Information systems → Spatialtemporal systems; Parallel and distributed DBMSs.

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

Cited by 35 publications

References 84 publications

Serverless Geospatial Data Processing Workflow System Design

Serverless Geospatial Data Processing Workflow System Design

Using a DBMS based on various data models in solving problems of cartography and geoinformatics

A Survey on Spatio-temporal Data Analytics Systems

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