Analysis of Big Data technologies for use in agro-environmental science
Recent developments like the movements of open access and open data and the unprecedented growth of data, which has come forward as Big Data, have shifted focus to methods to effectively handle such data for use in agro-environmental research. Big Data technologies, together with the increased use of cloud based and high performance computing, create new opportunities for data intensive science in the multi-disciplinary agro-environmental domain. A theoretical framework is presented to structure and analyse data-intensive cases and is applied to three case studies, together covering a broad range of technologies and aspects related to Big Data usage. The case studies indicate that most persistent issues in the area of data-intensive research evolve around capturing the huge heterogeneity of interdisciplinary data and around creating trust between data providers and data users. It is therefore recommended that efforts from the agro-environmental domain concentrate on the issues of variety and veracity.
| Main Authors: | , , , , |
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| Format: | Article/Letter to editor biblioteca |
| Language: | English |
| Subjects: | Agriculture, Big Data, Data integration, Forestry, Interdisciplinary research, Semantics, |
| Online Access: | https://research.wur.nl/en/publications/analysis-of-big-data-technologies-for-use-in-agro-environmental-s |
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| Summary: | Recent developments like the movements of open access and open data and the unprecedented growth of data, which has come forward as Big Data, have shifted focus to methods to effectively handle such data for use in agro-environmental research. Big Data technologies, together with the increased use of cloud based and high performance computing, create new opportunities for data intensive science in the multi-disciplinary agro-environmental domain. A theoretical framework is presented to structure and analyse data-intensive cases and is applied to three case studies, together covering a broad range of technologies and aspects related to Big Data usage. The case studies indicate that most persistent issues in the area of data-intensive research evolve around capturing the huge heterogeneity of interdisciplinary data and around creating trust between data providers and data users. It is therefore recommended that efforts from the agro-environmental domain concentrate on the issues of variety and veracity. |
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