  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, we
suggest representing the data by (spatial) functions, in particular by
polynomials. Given a (sampled) set of values, we 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. We present two solutions, one that extends
the Welch-Berlekamp technique in the case of multidimensional data, and copes
with discrete noise and Byzantine data, and the other based on Arora and Khot
techniques, extending them in the case of multidimensional noisy and Byzantine
data.
