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

Abstract

Abstract-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.

Efficient Identification of Common Subsequences from Big Data Streams Using Sliding Window Technique

“…The stream data processing is to understand data and to retrieve useful information from it. Various methods are designed to deal with big data [4][5] [6]. The challenges include accuracy, efficiency and availability.…”