ConDense: Managing Data in Community-driven Mobile Geosensor Networks
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Infinite nature of sensor data poses a serious challenge for query processing even in a cloud infrastructure. Model-based sensor data approximation reduces the amount of data for query processing, but all modeled segments need to be scanned, in the worst c ...
Efficiently querying data collected from Large-area Communitydriven Sensor Networks (LCSNs) is a new and challenging problem. In our previous works, we proposed adaptive techniques for learning models (e.g., statistical, non-parametric, etc.) from such dat ...
Effectively managing the data generated by community-driven mobile geo-sensor networks is a new and challenging problem. One important step for managing and querying sensor network data is to create abstractions of the data in the form of models. These mod ...
Conventional data warehouses employ the query-at-a-time model, which maps each query to a distinct physical plan. When several queries execute concurrently, this model introduces contention, because the physical plans—unaware of each other—compete for acce ...
