Anticipatory Clustering

M. Goller and M. Schrefl (Austria)


Data warehouse, Agriculture, OLAP, Cotton, Pest Scouting


First step towards understanding any agricultural system is the comprehension of relationships between the system and numerous physical, chemical and biological factors influencing it. Any decision regarding such systems requires analytical exploration of the involved data. The exploration task is to be supported by an efficient data storage and retrieval mechanism. In this paper we have presented the case of an Agri data warehouse for this purpose. We have briefly discussed the process we adopted for establishing the data warehouse encompassing pest, pesticide and metrological data. We have also shown how implementing an OLAP tool on top of the Agri data warehouse resulted in interesting findings from a decision support point of view.

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