Performance Optimisation in Sharing Big Geoscience Data

Abstract

Online Geoscience data sharing plays a key role in enabling organizations to make informed decisions. Geoscience data covers both spatial and non-spatial datasets. To maximize the benefits, structured data workflows and frameworks governing the data has to be continually experimented. Reproducibility of enhanced frameworks driving defined data provenance that govern the datasets enables efficiency of improved data. However, achieving data sharing of multiple geoscience datasets with different provenance in practice is problematic – previous findings signify issues of approaches used to outline data visualization due to uncontrolled changes on the input data used, internet of things (IoT) devises and algorithms used in ascertaining data correctness. The resulting problems fall within the category of big data issues. In this paper we present a framework that addresses geoscience big data solutions through data visualization based on the structured datasets. The framework is such that it addresses performance optimization, data input correctness and redundancy. The resulting approach is not constrained by the type of data format or size. The results are evaluated through an extensive set of experiments that validate the approach and highlight the key benefits of the proposed framework. This included ways of reducing data redundancy and correctness based on visualization approach.