Evaluating probabilistic queries over imprecise data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
PLACE: a query processor for handling real-time spatio-temporal data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
OpenSense: open community driven sensing of environment
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
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Efficiently 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.