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
This paper addresses the problem of forecasting various product demands of main distribution warehouses. Demand forecasting is the activity of building forecasting models to estimate the quantity of a product that customers will purchase. It is affected from numerously different factors such as warehouse region size, customer count, product type etc. When the number of the distribution warehouses and products increases, it becomes considerably hard to estimate the demand of customers. In this study, we provide an appropriate methodology for demand forecasting which is capable of overcoming the aforementioned limitations while providing a high estimation accuracy. The proposed methodology clusters similar warehouses according to their sale behavior using bipartite graph clustering. After that, hybrid forecasting phase which combines moving average model and Bayesian Network machine learning algorithm is applied. Our experimental results on real data set show that this approach considerably improves the forecasting performance.
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