e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.

This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.

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Efficient & Accurate Scheduling Algorithm For Cloudera Hadoop


the term immense data was coined to capture which suggests of this rising trend. To boot to its sheer volume, immense data to boot exhibits completely different distinctive characteristics as compared with ancient data. For instance, immense data is typically unstructured and wish extra amount analysis. This development incorporates new system architectures for data acquisition, transmission, storage, and large-scale process mechanisms. Recent technological advancements have semiconductor diode to a deluge of information from distinctive domains (e.g., health care and sciatic sensors, user generated data, net and money corporations, and supply chain systems). the buildup of information over the past twenty years has enlarged to large volumes. Apache Hadoop have introduced a economical and possible tool for distributed computing of such immense data for filtering and extracting massive volumes of knowledge. MapReduce can be a good used parallel computing framework for giant scale process. The two major performance metrics in MapReduce area unit job execution time and cluster production. MapReduce uses inventory accounting job programming by default and completely different programming algorithms area unit being introduced in proprietary domain. This work introduces a metric primarily based programming algorithmic rule to reinforce the potency and utilization of the server resources.

  • IN
    Data keywords
    • Hadoop
    • mapreduce
    • knowledge
    • distributed computing
    Agriculture keywords
    • supply chain
    Data topic
    • big data
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
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      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.