A cyber-physical algorithm for selecting a prevalent element from big data streams

Adi Alhudhaif, Tong Yan, Simon Berkovich


The paper presents a new algorithm for processing big data streams, which mimics the surmised physical design in the brain. The algorithm is very suitable for extracting prevalent information items, even at rather low frequencies of about several percents. The developing data driven process can be used to effectually realize various types of large-scale computational intelligence operations.


majority algorithm; stream processing; frequent items; big data processing

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