Big data interpolation using functional representation

Hadassa Daltrophe, Shlomi Dolev, Zvi Lotker

Research output: Contribution to journalArticlepeer-review


Given a large set of measurement data, in order to identify a simple function that captures the essence of the data, we suggest representing the data by an abstract function, in particular by polynomials. We interpolate the datapoints to define a polynomial that would represent the data succinctly. 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 (Error correction for algebraic block codes, 1986) to eliminate the outliers appearance in the case of multidimensional data, and copes with discrete noise and Byzantine data; and the other solution is based on Arora and Khot (J Comput Syst Sci 67(2):325–340, 2003) method which handles noisy data, and we have generalized it in the case of multidimensional noisy and Byzantine data.

Original languageEnglish
Pages (from-to)213-225
Number of pages13
JournalActa Informatica
Issue number3
StatePublished - 1 May 2018


  • Big data
  • Data aggregation
  • Data interpolation
  • Representation
  • Sampling

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Networks and Communications


Dive into the research topics of 'Big data interpolation using functional representation'. Together they form a unique fingerprint.

Cite this