GSTF Journal on Computing (JoC)

, 4:1

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

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

  • Adi AlhudhaifAffiliated withDepartment of Computer Science, The George Washington University Email author 
  • , Tong YanAffiliated withDepartment of Computer Science, The George Washington University
  • , 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