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? E-mail: saket.sathe@epfl.ch