On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems

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 i...

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Main Authors: Nimmagadda, Shastri, Dreher, Heinz, Rudra, Amit
Format: Journal Article
Published: Springer 2016
Online Access:http://hdl.handle.net/20.500.11937/81391
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author Nimmagadda, Shastri
Dreher, Heinz
Rudra, Amit
author_facet Nimmagadda, Shastri
Dreher, Heinz
Rudra, Amit
author_sort Nimmagadda, Shastri
building Curtin Institutional Repository
collection Online Access
description 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.
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institution Curtin University Malaysia
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spelling curtin-20.500.11937-813912021-02-04T03:31:43Z On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems Nimmagadda, Shastri Dreher, Heinz Rudra, Amit 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. 2016 Journal Article http://hdl.handle.net/20.500.11937/81391 10.1007/s10666-016-9504-8 Springer restricted
spellingShingle Nimmagadda, Shastri
Dreher, Heinz
Rudra, Amit
On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems
title On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems
title_full On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems
title_fullStr On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems
title_full_unstemmed On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems
title_short On a Holistic Modelling Approach for Managing Carbon Emission Ecosystems
title_sort on a holistic modelling approach for managing carbon emission ecosystems
url http://hdl.handle.net/20.500.11937/81391