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In this work, we present a framework, denoted ADAGA -- P*, for processing complex queries and for managing sensor-field regression models. The proposed mechanism builds and instantiates sensor-field models. Thus ADAGA -- P* makes query engines able to answer complex queries such as give the probability of rain for the next two days in the city of Fortaleza. On the other hand, it is well known that minimizing energy consumption in a Wireless Sensor Network (WSN) is a critical issue for increasing the network lifetime. An efficient strategy for saving power in WSNs is to reduce the data volume injected into the network. For that reason, ADAGA -- P* implements an in-network data prediction mechanism in order to avoid that all sensed data have to be sent to fusion center node (or base station). Thus, sensor nodes only transmit data which are novelties for a regression model applied by ADAGA -- P*. Experiments using real data have been executed to validate our approach. The results show that ADAGA -- P* is quite efficient regarding communication cost and the number of executed float-point operations. In fact, the energy consumption rate to run ADAGA -- P* is up to 14 times lower than the energy consumed by kernel distributed regression for an RMSE difference of 0.003.