Author:
Alhudhaif Adi,Yan Tong,Berkovich Simon
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.
Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Manyika J, Chui M, Brown B, et al. Big data: The next frontier for innovation, competition, and productivity. 2011.
2. Zikopoulos P, Eaton C. Understanding big data: Analytics for enterprise class hadoop and streaming data. McGraw-Hill Osborne Media, 2011.
3. Adi Alhudhaif, Tong Yan and Simon Berkovich. “On the organization of cluster voting with massive distributed streams”, in Proceedings of the 5th international Conference on Computing for Geospatial Research & Application, Washington, D.C., 2014.COM.Geo
4. Alon, Noga, Yossi Matias, and Mario Szegedy. “The space complexity of approximating the frequency moments.” Proceedings of the 28th annual ACM symposium on Theory of computing. ACM, 1996
5. Babcock, Brian;Babu, Shivnath;Datar, Mayur;Motwani, Rajeev;Widom, Jennifer (2002), “Models and issues in data stream systems”,Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2002), pp. 1–16, doi:10.1145/543613.543615.