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.

You can access and play with the graphs:

Discover all records
Home page


A Retail Demand Forecasting Model Based on Data Mining Techniques


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.

  • TR
  • Istanbul_Tech_Univ_ITU (TR)
  • Idea_Technol_Solut (TR)
Data keywords
  • machine learning
Agriculture keywords
  • supply chain
Data topic
  • big data
  • modeling
Document type

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

Institutions 10 co-publis
  • Istanbul_Tech_Univ_ITU (TR)
Powered by Lodex 8.20.3
logo commission europeenne
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.