An Architecture for Cost Optimization in the Processing of Big Geospatial Data in Public Cloud Providers

Bachiega, João; Reis, Marco Antonio Sousa; Holanda, Maristela; Araújo, Aletéia

doi:10.1109/bigdatacongress.2018.00032

Cited by 1 publication

(1 citation statement)

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“…As shown in the ‘papers’ published as a result of this research (Bachiega, Reis, Araujo, & Holanda, 2017a, 2017b; Bachiega, Reis, Holanda, & Araujo, 2017, 2018), indexing is the most expensive task for processing big geospatial data, so it is critical to observe the behavior of each database type, both in size and content, when searching for the most appropriate indexing. It is also important to note what are the best‐performing times and cost indices for each operation and geographic query.…”

Section: Methodsmentioning

confidence: 97%

A proposal to minimize the cost of processing big geospatial data in public cloud providers

Bachiega

Holanda

Araújo

2021

Transactions in GIS

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Spatial data represent abstractions of real‐world entities and can be obtained in various ways. They have properties that differentiate them from other types of data, such as a complex structure and dynamism. In recent years with the increasing volume of spatial data, referred to as “big geospatial data,” some tools have been developed to process such data efficiently, such as SpatialHadoop. The use of appropriate data indices based on the data set to be processed, as well as queries and operations to be performed, is essential for the optimal performance of these applications. In particular, since public cloud providers’ charges are based on the resources used, it is imperative to optimize application execution in order to avoid unnecessary expense. This article proposes the use of six conditions that seek to minimize the cost of processing big geospatial data in public cloud providers. The tests performed demonstrate that the use of these conditions and the choice of the lowest‐cost provider can reduce the total processing cost by up to 41%.

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Section: Methodsmentioning

confidence: 97%