Conceptual design and implementation of spatial data warehouses integrating regular grids of points

Bimonte, Sandro; Zaamoune, Mehdi; Beaune, Philippe

doi:10.1080/17538947.2016.1266040

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…Performing novel system‐centric comparisons to evaluate the performance of these versions against other SASs is also another interesting future work. An additional future work relies on analyzing SASs by considering their support for the raster model, whose storage occurs continuously and is used to model geographic phenomena that are constantly changing in space, such as altitude and temperature 82 . Finally, in this article we provide a user‐centric comparison of SASs that are developed on top of Hadoop and Spark.…”

Section: Discussionmentioning

confidence: 99%

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

2020

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Summary Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system‐centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user‐centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user‐centric comparison of the following SASs based on Hadoop and Spark: Hadoop‐GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real‐world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.

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Section: Discussionmentioning

confidence: 99%

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

2020

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“…Significant quantities of data are paired with performance issues in RDBMS for storage and unstructured data, whilst geospatial data queries are fundamental challenges associated with GIS. Given that efficient access to Internet-based geospatial data is critical in performing on-demand and real-time queries, several studies have recently examined the use of the cloud computing paradigm in solving these issues [45]. Driven by the substantial computational and storage-related capacities of cloud computing infrastructures, many studies have applied NoSQL DBMSs, such as MongoDB and HBase.…”

Section: Storage Geospatial Big Datamentioning

confidence: 99%

Review on Integrating Geospatial Big Datasets and Open Research Issues

et al. 2021

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Big data and geographic information systems (GIS) are two technologies that have increasingly influenced many areas in the last 10 years and will continue to improve and help solve serious global problems, such as consequences of climate change or global pandemics. A wide spectrum of GIS applications interacts with the continuous growth of geospatial big data sources to drive precise and informed decisions. Geospatial big data integration is designed to accomplish the compatibility of distinct geospatial datasets regardless of their spatial coverage. The large number of geospatial big data sources demand effective data integration for storing and handling such datasets, which will be used for geospatial data analysis and visualization. For instance, risk management datasets related to healthcare and the environment are heterogeneous and disparate. Obtaining a unified view of such geospatial big datasets is complicated and challenging, especially if we consider problems related to healthcare pandemics and environmental disasters. Hence, before we can attempt to predict and mitigate processes occurring in these domains, we must realize that geospatial big data integration is crucial in consolidating datasets. We explore and discuss issues involved in integrating geospatial big datasets in this study. We then classify big data integration processes into three categories, namely, data warehousing, data transformation and integration methods. Furthermore, several research challenges focused on geospatial big data, big earth data, data warehousing, data transformation and linked data are presented. Lastly, open research issues and emerging trends that require in-depth investigations in the near future are highlighted in this study.INDEX TERMS Big data integration, geographic information system (GIS), geospatial big data.

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SQL or NoSQL? Which Is the Best Choice for Storing Big Spatio-Temporal Climate Data?

Lian

Miao

McGuire

et al. 2018

Lecture Notes in Computer Science

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Conceptual design and implementation of spatial data warehouses integrating regular grids of points

Abstract: Conceptual design and implementation of spatial data warehouses integrating regular grids of points Sandro Bimonte (a), Mehdi Zaamoune (a) and Philippe Beaune (b) a-Technologies and information systems for agricultural systems Departement, Irstea,

Cited by 6 publications

References 30 publications

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective

Review on Integrating Geospatial Big Datasets and Open Research Issues

SQL or NoSQL? Which Is the Best Choice for Storing Big Spatio-Temporal Climate Data?

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