Anomaly detection in agri warehouse construction
Andrew L. McCarren, Suzanne McCarthy, Conor Sullivan, Mark Roantree · Proceedings of the Australasian Computer Science Week Multiconference · 2016
As with many sectors, strategists and decision makers in the agricultural sector have a requirement to predict key measures such as product and feed pricing in order to maintain their position and, in some cases, to survive in their industry. Predictive algorithms in the area of Agri Analytics have shown to be very difficult due to the wide range of parameters and often unpredictable nature of some of these variables. Improving the predictive capability of Agri planners requires access to up-to-date external information in addition to the analyses provided by their own in-house databases. This motivates the need for an Agri Data Ware-house together with appropriate cleaning and transformation processes. However, with the availability of rich and wide ranging sources of Agri data now available online, there is a strong motivation to process as much current, online information as possible. In this work, we introduce the Agri Data Warehouse built for the DATAS project which not only harvests from a large number of online sources but also adopts an anomaly detection and labelling process to assist transformation and loading into the warehouse.