Milk Miner


The importance of "true and meaningful information" that will be extracted from these growing masses of data is increasing day by day. However, the ability to collect and store data at high capacities and intensities has a real potential for processing this valuable data. Milk Miner project has developed an analysis and learning system that will contribute to improving the conditions in the farms by analyzing data collected from milk production farms with data mining and logical reasoning techniques. Project outputs are used as an analysis and learning system.
With the software technologies developed by Triodor, various data and key performance metrics from more than 15,000 farms in more than 50 countries around the world are collected daily in a centralized database on a server and presented to the user. By analyzing this extensive database and various outsourced data and parameters and identifying the concealed relationships among them, it is possible to develop important developments in many aspects such as milk yield, sustainability of production, product quality. The effect of the change of feed on the milk production on the farms having the same characteristics, the factors affecting the performance of the dairy robot on the farms working in the same conditions, the factors affecting the amount of milk separated in the farms working on the same conditions, what is the difference between the ruminant average such as the identification of changes depending on factors.

MS SQL, Eclipse Java EE IDE, JBoss Maven Integration 1.0, JavaServer Faces 2.0, JavaScript 1.0, RichFaces, Weka Library, Tomcat v7.0 Server, JSON