Big Data

Data Interpolation: An Efficient Sampling Alternative for Big Data Aggregation

Download Now Date Added: Oct 2012
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Given a large set of measurement sensor data, in order to identify a simple function that captures the essence of the data gathered by the sensors, the authors suggest representing the data by (spatial) functions, in particular by polynomials. Given a (sampled) set of values, they interpolate the datapoints to define a polynomial that would represent the data. The interpolation is challenging, since in practice the data can be noisy and even Byzantine, where the Byzantine data represents an adversarial value that is not limited to being close to the correct measured data.