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
Multi-granular fact tables are used to store and query data at different levels of granularity. In order to collect data in multi-granular fact tables on a resource-constrained system and to keep it for a long time, we gradually aggregate data to save space, however, still allowing analysis queries, for example, for maintenance purposes etc. to work and generate valid results even after aggregation. However, ineffective means of data aggregation is one of the main factors that can not only reduce performance of the queries but also leads to erroneous reporting. This paper presents effective methods for data reduction that are developed to perform gradual data aggregation in multi-granular fact tables on resource-constrained systems. With the gradual data aggregation mechanism, older data can be made coarse-grained while keeping the newest data fine-grained. This paper also evaluates the proposed methods based on a real world farming case study.
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