Development of a Total Environment Data Science Approach in a Big Data Scale
|dc.identifier.citation||Nimmagadda, S. and Reiners, T. and Rudra, A. 2017. Development of a Total Environment Data Science Approach in a Big Data Scale. Procedia Computer Science. 112: pp. 1891-1900.|
We use the Big Data paradigm, as a driving mechanism of an integrated research framework. As a case study, we consider analysing various ecological systems and their connectivity in the framework. An unknown coexistence among different species and lack of knowledge on their sustainability motivate us for undertaking the current research. For describing the recycling systems in nature, an articulated design science research (DSR) framework is necessary for which we have constructed data models for composite lithosphere-atmosphere-biosphere-hydrosphere ecosystem (LABHE). The unstructured big-size environmental data sources and their anomalies existing in nature are taken advantage of, to describe various constructs, compute models and validate them by DSR guidelines. For this purpose, the domain ontology artefacts are drawn and integrated into a warehouse approach to compute an environmental metadata and interpret it in different knowledge domains. The data models and the proposed integrated framework facilitate the environment explorers for planning and management of environmental resources worldwide. The Big Data associated LABHE bring out new knowledge and its interpretation in a variety of environmental data map and plot views. The constructs, models and methodologies used in the current domain application are research deliverables for the total environment researchers and explorers.
|dc.title||Development of a Total Environment Data Science Approach in a Big Data Scale|
|dcterms.source.title||Procedia Computer Science|
|dcterms.source.series||Procedia Computer Science|
|curtin.department||School of Information Systems|