On Clusterization in "Big Data" Streams

Dr. Simon Y. Berkovich
Professor of Engineering and Applied Science,
Department of Computer Science, The George Washington University,
Senior Advisor in COM.Geo Advisory Board

Big Data refers to the rising flood of digital data from many different sources. "Immense" and "diverse" are two important characteristics of the "Big Data". Coping with the expanding variety of the Big Data requires a radical change in the philosophy of the organization of information processing. The Big Data approach has to modify the underlying computational model in order to manage the uncertainty in the access to information items in a huge nebulous environment. In this talk, we introduce a novel method for on-the-fly clusterization of amorphous data from diverse sources. It is especially suitable for the Big Data computational model as it materializes the requirement of purposeful selection of information items in unsteady framework of cloud computing and stream processing. Furthermore, the uncertainties in relation to the considered method of clusterization are moderated due to the idea of the bounded rationality, an approach that does not require a complete exact knowledge for sensible decision-making. >>