Taming the Evolution of Big Data and its Technologies in BigGIS - A Conceptual Architectural Framework for Spatio-Temporal Analytics at Scale

Wiener, Patrick; Šimko, Viliam; Nimis, Jens

doi:10.5220/0006334200900101

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Meta-reviews discuss the platforms and their capabilities. [14][15][16][17][18] These papers are orientated towards the broadest communities and focus on the platform capabilities not platform performance.…”

Section: Meta Reviewsmentioning

confidence: 99%

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

Haynes¹,

Mitchell²,

Shook³

2020

Preprint

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Technologies around the world produce and interact with geospatial data instantaneously, from mobile web applications to satellite imagery that is collected and processed across the globe daily. Big raster data allows researchers to integrate and uncover new knowledge about geospatial patterns and processes. However, we are also at a critical moment, as we have an ever-growing number of big data platforms that are being co-opted to support spatial analysis. A gap in the literature is the lack of a robust framework to assess the capabilities of geospatial analysis on big data platforms. This research begins to address this issue by establishing a geospatial benchmark that employs freely accessible datasets to provide a comprehensive comparison across big data platforms. The benchmark is a critical for evaluating the performance of spatial operations on big data platforms. It provides a common framework to compare existing platforms as well as evaluate new platforms. The benchmark is applied to three big data platforms and reports computing times and performance bottlenecks so that GIScientists can make informed choices regarding the performance of each platform. Each platform is evaluated for five raster operations: pixel count, reclassification, raster add, focal averaging, and zonal statistics using three different datasets.

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Section: Meta Reviewsmentioning

confidence: 99%

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

Haynes¹,

Mitchell²,

Shook³

2020

Preprint

View full text Add to dashboard Cite

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“…Kafka provides a set of APIs which allow developers to plug into the platform: Kafka is a key pillar in Java-based Big Data and IoT architectures (e.g., [37]). It is able to integrate with Apache Hadoop (http://hadoop.apache.org/) and other related Apache frameworks such as Storm and Spark (see above).…”

Section: Apache Kafkamentioning

confidence: 99%

Geospatial IoT—The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures

Rieke¹,

Bigagli

Herlé

et al. 2018

IJGI

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The nature of contemporary spatial data infrastructures lies in the provision of geospatial information in an on-demand fashion. Although recent applications identified the need to react to real-time information in a time-critical way, research efforts in the field of geospatial Internet of Things in particular have identified substantial gaps in this context, ranging from a lack of standardisation for event-based architectures to the meaningful handling of real-time information as “events”. This manuscript presents work in the field of event-driven architectures as part of spatial data infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of spatial data infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches—developed in different research projects—to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall event-driven spatial data infrastructure.

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“…[36]). It is able to integrate with Apache Hadoop 9 and other related Apache frameworks such as Storm and Spark (see above).…”

Section: Apache Kafkamentioning

confidence: 99%

GeoSpatial IoT - the Need for an Event-Driven Spatial Data Infrastructure

Rieke¹,

Bigagli²,

Herlé³

et al. 2018

Preprint

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Abstract:The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified t he n eed t o react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as "events". This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches -developed in different research projects -to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

show abstract

Taming the Evolution of Big Data and its Technologies in BigGIS - A Conceptual Architectural Framework for Spatio-Temporal Analytics at Scale

Cited by 5 publications

References 8 publications

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

Developing the Geospatial Big Data Benchmark: A Comparative Framework for Evaluating Raster Analysis on Big Data Platforms

Geospatial IoT—The Need for Event-Driven Architectures in Contemporary Spatial Data Infrastructures

GeoSpatial IoT - the Need for an Event-Driven Spatial Data Infrastructure

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