TY - GEN
T1 - Big data interpolation an efficient sampling alternative for sensor data aggregation (Extended Abstract)
AU - Daltrophe, Hadassa
AU - Dolev, Shlomi
AU - Lotker, Zvi
N1 - Funding Information: Partially supported by a Russian Israeli grant from the Israeli Ministry of Science and Technology #85387301-“Algorithmic approaches to energy savings” and the Russian Foundation for Basic Research, the Rita Altura Trust Chair in Computer Sciences, the Lynne and William Frankel Center for Computer Sciences, Israel Science Foundation (grant number 428/11), Cabarnit Cyber Security MAGNET Consortium, Grant from the Institute for Future Defense Technologies Research named for the Medvedi of the Technion, MAFAT, and Israeli Internet Association.
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
KW - Big Data
KW - Data Interpolation
KW - Sampling
KW - Sensor Data Aggregation
KW - Spatial Sensor Inputs
UR - http://www.scopus.com/inward/record.url?scp=84872451822&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-642-36092-3_8
DO - https://doi.org/10.1007/978-3-642-36092-3_8
M3 - Conference contribution
SN - 9783642360916
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 66
EP - 77
BT - Algorithms for Sensor Systems - 8th International Symposium on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities, ALGOSENSORS 2012, Revised Selected Papers
PB - Springer Verlag
T2 - 8th International Symposium on Algorithms for Sensor Systems, Wireless Ad Hoc Networks and Autonomous Mobile Entities, ALGOSENSORS 2012
Y2 - 13 September 2012 through 14 September 2012
ER -