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Building an Active Semantic Data Warehouse for Precision Dairy Farming

Authors: C. Schütz, S. Schausberger, M. Schrefl
Paper: Schu18a (2018)
Citation: Journal of Organizational Computing and Electronic Commerce (JOCEC), Issue on Business Intelligence and Analytics Case Studies, Vol. 28, No. 2, Taylor & Francis, ISSN 1091-9392, Online ISSN 1532-7744, DOI: http://doi.org/10.1080/10919392.2018.1444344, pp. 122-141, 2018.
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Abstract (English):

Digitalization of agricultural technology has led to the emergence of precision dairy farming which strives for the simultaneous improvement of productivity as well as animal well-being in dairy farming through advanced use of technology, e.g., movement sensors and milking parlors, to monitor, control, and improve dairy production processes. The data warehouse serves as the appropriate technology for effective and efficient data management in precision dairy farming, which is paramount to the success of precision dairy farming. This paper presents a joint effort between industry and academia on the experimental development of an active semantic data warehouse to support BI and business analytics in precision dairy farming. The research follows an action research approach, deriving lessons for theory and practice from a set of actions taken during the projects. Among these actions are the development of a loading stage to facilitate data integration, the definition of an analysis view and the introduction of semantic OLAP patterns to facilitate analysis itself, and analysis rules to automate periodic analyses. The large volumes of generated sensor data in precision dairy farming required careful decision-making about the appropriate level of detail of the data stored in the data warehouse. Semantic technologies played a key role in rendering analysis accessible to end users.

Keywords: Business intelligence, big data analysis, data integration, sensor data, precision livestock farming, action research