Building an active semantic data warehouse for precision dairy farming. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Schuetz, Christoph G.; Schausberger, Simon; Schrefl, Michael
• Is Part Of: Journal of organizational computing and electronic commerce. Volume 28:Issue 2(2018)
• Page Start: 122
• Page End: 141
• Publication Date: 2018-04-03
• Subjects: Business intelligence -- big data analysis -- data integration -- sensor data -- precision livestock farming -- action research; Management -- Data processing -- Periodicals; Electronic commerce -- Periodicals; Electronic data interchange -- Periodicals; Business enterprises -- Computer networks -- Periodicals; 658.0505
• DOI: 10.1080/10919392.2018.1444344
• Languages: English
• ISSNs: 1091-9392

Abstract

ABSTRACT: 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 such as 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, 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 business intelligence 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 in the course of the project. Among these actions are the development of a loading stage to facilitate data integration, the definition of an analysis view as well as the introduction of semantic OLAP patterns to facilitate analysis, and analysis rules to automate periodic analyses. The large volumes of generated sensor data in precision dairy farming required careful decision-making concerning 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.