Building Spatiotemporal Cloud Platform for Supporting Gis Application

Song, Weiwei; Jin, Baoxuan; Li, S. H.; Xiong, Wei; Li, D.; Hu, Fei

doi:10.5194/isprsannals-ii-4-w2-55-2015

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…The use of the MapReduce [Dean and Ghemawat 2008] programming model caused a revolution in the processing and management of spatiotemporal data [Guo et al 2017]. MapReduce has facilitated the development of works for data mining of spatiotemporal data [Lin et al 2013;de Assis et al 2017;Song et al 2015;Patterson 2011] and3D objects [Van Den Bergh et al 2012].…”

Section: • 275mentioning

confidence: 99%

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Oliveira

Rodrigues

Martins

2021

jidm

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Spatiotemporal data has always been big data. In these days, big data analytics for spatiotemporal data is receiving considerable attention to allow users to analyze huge amounts of data. Traditional big data platforms cannot handle all the challenges of processing spatio-temporal data. Although some big data platforms have been proposed to process a massive volume of spatiotemporal data, neither is considered a clear winner for all possible scenarios. This paper presents the SmarT query engine, a machine learning-based solution that chooses the best big data platform for processing spatiotemporal queries on the fly. In a detailed experimental evaluation, considering the Apache Spark, Elasticsearch, and SciDB big data platforms, the response time decreased up to 22% when using SmarT.

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Section: • 275mentioning

confidence: 99%

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

Oliveira

Rodrigues

Martins

2021

jidm

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“…Some researchers have studied how to use workflow technology to combine algorithm functions in existing GIS software to solve complex problems in environmental research [7][8][9]. Some have studied how to combine geographic information services on the internet to obtain more powerful geographic information services [10][11][12]. However, these methods do not adequately address business process extensions in a cloud GIS environment.…”

Section: Introductionmentioning

confidence: 99%

An On-Demand Scalable Model for Geographic Information System (GIS) Data Processing in a Cloud GIS

Zhang

et al. 2019

IJGI

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With the rapid development of information processing technology and continuously evolving technological hardware and software, the scale of spatial data has grown exponentially. This has necessitated that stricter requirements be placed on the way data is processed. The traditional single-machine centralized data processing method is gradually being replaced by the network-based cloud geographic information system (GIS) mode. However, the information processing method of the business system in the existing spatial information network environment is difficult to expand, which limits the application of the business system. This paper proposes an on-demand and extended model for the GIS data processing procedure that considers the three-way separation of algorithm development, business processes, and the operational interface, and can implement an on-demand expansion of business processes by process modeling the business and task scheduling the workflow engine. This can expand the scope of business systems and improve the efficiency of business system construction.

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“…Aji et al (2013) presented Hadoop GIS, depending on a spatial data warehousing system, to tackle large-scale spatial queries. Song et al (2015) built a spatiotemporal cloud platform, which uses HDFS for managing image data, MapReduce computing service, and workflow of high-performance geospatial analysis, to provide optimization scaling algorithms and visualization.…”

Section: Introductionmentioning

confidence: 99%

A spatiotemporal algebra in Hadoop for moving objects

Bakli

Sakr

Soliman

2018

Geo-spatial Information Science

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Spatiotemporal data represent the real-world objects that move in geographic space over time. The enormous numbers of mobile sensors and location tracking devices continuously produce massive amounts of such data. This leads to the need for scalable spatiotemporal data management systems. Such systems shall be capable of representing spatiotemporal data in persistent storage and in memory. They shall also provide a range of query processing operators that may scale out in a cloud setting. Currently, very few researches have been conducted to meet this requirement. This paper proposes a Hadoop extension with a spatiotemporal algebra. The algebra consists of moving object types added as Hadoop native types, and operators on top of them. The Hadoop file system has been extended to support parameter passing for files that contain spatiotemporal data, and for operators that can be unary or binary. Both the types and operators are accessible for the MapReduce jobs. Such an extension allows users to write Hadoop programs that can perform spatiotemporal analysis. Certain queries may call more than one operator for different jobs and keep these operators running in parallel. This paper describes the design and implementation of this algebra, and evaluates it using a benchmark that is specific to moving object databases.;

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Building Spatiotemporal Cloud Platform for Supporting Gis Application

Cited by 8 publications

References 15 publications

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

SmarT: Machine Learning Approach for Efficient Filtering and Retrieval of Spatial and Temporal Data in Big Data

An On-Demand Scalable Model for Geographic Information System (GIS) Data Processing in a Cloud GIS

A spatiotemporal algebra in Hadoop for moving objects

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