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Model-Based Query Processing Over Uncertain Data (in ICDE 2011) Raw Sensor Data Inference of time-varying probability distributions Creating probabilistic.

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Presentation on theme: "Model-Based Query Processing Over Uncertain Data (in ICDE 2011) Raw Sensor Data Inference of time-varying probability distributions Creating probabilistic."— Presentation transcript:

1 Model-Based Query Processing Over Uncertain Data (in ICDE 2011) Raw Sensor Data Inference of time-varying probability distributions Creating probabilistic views Query Processing Characterizing Uncertainty in Time-Series Data Pollution data is an example of uncertain time-series data

2 Multi-model Query Processing in Mobile Geosensor Networks Our Approach – Middle layer that produces a model cover from a set of regression models on an area – Sensor data keeps updating the models – Queries operate on top of the models Advantages – Key mid-level abstraction helps in handling spurious updates to the data base – Specially suitable for uncontrolled sensory deployments (for ex., community sensing) – Minimizes data storage Intuition – Queries processed over models should yield accurate results than queries processed over raw values Mobile Sensor Data (Pollution Values) Model-based middle layer Mobile Sensor Data (Pollution Values) Continuous Moving Queries Give a (in car) pollution update every 30 mins Aggregate Queries CO X emitted yesterday in Lausanne center DBMS (storage of raw sensor values) DBMS (storage of raw sensor values)

3 Modeling Data from Large-area Community Sensor Networks (in IPSN 2012) Key contributions: – Estimation of model cover over large geographical areas (cities/urban spaces) – Maintaining the model cover over spatio- temporal evolution of the phenomenon Uncontrolled or semi-controlled mobility of the sensors Adaptive vs. Non-adaptive – Non-adaptive: Grid-based methods (GRIB) – Adaptive: Adaptive K-means (Ad-KMN) Experimental evaluation over to real datasets Adaptive K-means Overview of the framework

4 Multi-Model Approximation of Sensor Data (in MDM 2011)

5 Thank you. Questions?


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