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On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Nimmagadda, Shastri L., Dreher, Heinz V., Rudra, Amit
Environmental modeling and assessment 2016 v.21 no.6 pp. 763-801
carbon, carbon dioxide, ecosystems, greenhouse gas emissions, mining, models, prediction
Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO₂) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust “ontology-based multidimensional data warehousing and mining” approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes’ correlations, patterns, and trends that can help in predicting future forecast of CO₂ emissions worldwide.