Analysis of Change in Climate with Meta Data Problems Using Parallel and Distributed Analytics Approaches on Big Data Clouds

Abstract

The scientific analysis of change in climate as an intensive data subject has been enormously elaborated by the epoch of data analysis and other utter important advancements in technology. The rewards of analysis in metadata for various fields in the last years have proved the importance of metadata analysis and its vitality on the major problem of change in climate. As a field of emergence, change in climate has been always in the talks of the metadata analysis, implementation of methods and comprehensive researches are being implemented with different and utter importance topics in research. The scientific analysis related to change in climate has relied on huge amounts of analytical and informational data of computational power. Over the last decade, considerable growth in various kinds of data fields and relating analytical capacities resulting from these pread of digital technology categorized under the term ‘Big data’ have been recorded. The huge amounts of data for climate change are saved on Hadoop Distributed File System (HDFS) with usage of   algorithm known as Map-Reduce for applying and calculating the average (seasonal basis) of change in climate variables. Space-based auto-correlation change in the algorithm is presented in this review for visualizing the variances in the climatic conditions. The presented change in climate detection algorithm is in contrast with the subsisting viewpoints like pruned exact linear time method (PELT), binary segmenting method, and segment neighborhood method.

 The aim of this research is for presenting an interpretation of metadata in the study of change in climate over the previous decades by analyzing and interpreting the status quo of applications related to metadata in related studies of change in climate. The expectation of this paper is to be set out as a directory of recommendation for researchers with knowledge of this trending subject that may be used in guidance of futuristic researches and developments in the metadata field related to change in climate.