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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Data modeling for Precision Dairy Farming within the competitive field of operational and analytical tasks


The interdisciplinary concept of Precision Dairy Farming sets very high standards for data management. Special consideration during its implementation must therefore be given to support both operational and analytical data uses (e.g., OLAP). The inclusion of both data views results in the data modeling being a hybrid of two conceptual design models. In contrast to previous design concepts, we will assume a parallel modeling process for both views, which results in a shared logical data schema. This is the only way to effectively avoid redundancies and inconsistencies on both the schema and data levels. Using an ongoing application as an example, we will explain both methods and results. In doing so, we will make use of the Entity-Relationship Model (E/RM) for modeling operational data. We will also make use of E/RM's multi-dimensional extension, the multi-dimensional Entity-Relationship Model (mE/RM), for modeling analytical data. In order to meet all application-specific modeling requirements, however, new representation elements must be introduced. Therefore, we propose both a property window to describe the subject of analysis, and also a marker for temporal restrictions to the values of analysis structures as an extension of the mE/RM. Starting from the two conceptual models, we will then describe the logical modeling in a shared relational schema. Both the transformation of conceptual notation elements into relational structures and the creation of a required meta model will be explained during this step. The procedures discussed in this paper are important for a variety of tasks in the field of Precision Dairy Farming and beyond. (C) 2007 Elsevier B.V. All rights reserved.

  • DE
  • Univ_Halle_Wittenberg (DE)
  • Dresden_Univ_Technol (DE)
Data keywords
  • data warehouse
  • data model
  • data management
Agriculture keywords
  • farming
Data topic
  • information systems
  • modeling
  • sensors
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.