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

Cloud computing is a suitable platform for running applications to process big data. Currently, with the increase in the volume of geographic and spatial data volume, conceptualized as Big Geospatial Data, a variety of tools have been developed to efficiently process this data. The index applied to the dataset is an important aspect. This paper presents an architecture, supported by a Knownlegde Base and an Inference Engine, to process big geospatial data in public cloud providers with the ultimate goal of optimizing costs. The tests executed demonstrated that the rules created are capable of optimizing the total costs for processing large geospatial data up to 71%.