Document-Oriented Geospatial Data Warehouse: An Experimental Evaluation of SOLAP Queries

Ferro, Marcio; Fragoso, Rogerio C. P.; Fidalgo, Robson

doi:10.1109/cbi.2019.00013

Cited by 9 publications

(2 citation statements)

References 19 publications

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“…Due to GIS maturity in relational databases [29], SOLAP are often implemented as ROLAP. Nevertheless, recent studies also propose (S)OLAP implementation in NoSQL databases like documents stores [30], column-oriented databases [31] or RDF (resource description framework) graphs [32,33].…”

Section: Olap Implementationmentioning

confidence: 99%

A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

Kasprzyk

Devillet

2021

IJGI

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Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration.

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Section: Olap Implementationmentioning

confidence: 99%

A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

Kasprzyk

Devillet

2021

IJGI

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“…In related research on MongoDB, attention has focused on geospatial data management and storage [34][35][36][37][38][39][40][41][42][43] and on index development methods [29,[44][45][46]. Using MongoDB, several platforms and frameworks have been designed to solve diverse application demands.…”

Section: ] }mentioning

confidence: 99%

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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