Distributed Data Mining and Knowledge Management with Networks of Sensor Arrays

Environmental pollution control relies heavily on human expert judgment supported by historical data and scientific models. Telemonitoring by networks of arrays of heterogeneous sensors provides the opportunity for data mining models to be constructed from the historical data to supplement human expertise. This paper reports some progress made in the TELEMAC project by data mining. TELEMAC is concerned with enhancing the efficacy of anaerobic digestion in potentially unstable digesters. In the laboratory using full instrumentation it is possible to derive a good description of the digester state. With data mining it is possible to identify some constraints on sensor choice. This paper examines this data mining work from the perspective of a three layer Grid architecture to see what implications and requirements arise that could benefit the exercise of expert judgment. After placing the specific TELEMAC situation in a generic Grids context, we present a classified approach to attributes for metadata and indicate some examples of model resource discovery.