Yvonne Anne Pignolet, Stefan Schmid, et al.
Discrete Mathematics and Theoretical Computer Science
Effciently querying data collected from Large-area Community driven Sensor Networks (LCSNs) is a new and challenging problem. In our previous works, we proposed adaptive techniques for learning models (e.g., statistical, nonparametric, etc.) from such data, considering the fact that LCSN data is typically geo-temporally skewed. In this paper, we present a demonstration of EnviroMeter. EnviroMeter uses our adaptive model creation techniques for processing continuous queries on community-sensed environmental pollution data. Subsequently, it efficiently pushes current pollution updates to GPS-enabled smartphones (through its Android application) or displays it via a web-interface. We experimentally demonstrate that our model-based query processing approach is orders of magnitude efficient than processing the queries over indexed raw data. © 2013 VLDB Endowment.
Yvonne Anne Pignolet, Stefan Schmid, et al.
Discrete Mathematics and Theoretical Computer Science
G. Ramalingam
Theoretical Computer Science
Michael D. Moffitt
ICCAD 2009
Chidanand Apté, Fred Damerau, et al.
ACM Transactions on Information Systems (TOIS)