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.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
The research of universal data mining model system based on logistics data warehouse and application
This paper proposes an integration system to the logistics enterprise information system in distributed heterogeneous environment. We establish a framework structure of universal data mining system based on logistics data warehouse and apply the proposed system into practical management of logistics and shipping enterprises. Feature. extraction and data sample classification from large-scale data warehouse is realized by constructing the logical feature space and generating the rule and pattern about the logical feature sub-space, which can help data mining system earn necessary knowledge about a specific part of a real or abstract information and further use the knowledge to match data mining models. The discussed results of illustrative example and numerical simulation show that the developed models system and methodologies can be useful and applicable to realize the intelligent decision_support system in the logistics enterprise and supply chain management.
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