Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. But sensors do not exhaustively represent the data in the real world. The authors have to map the raw sensor readings onto physical reality. In this paper, they enrich interactive sensor querying with statistical modeling techniques. They demonstrate that such models can help provide answers that are both more meaningful and more efficient to compute in both time and energy. Their approach works on several real world sensor network data sets demonstrating that their model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques.