Discovering topological patterns in time-series big graph

Abstract

Discovering topological patterns in a Big Data Graph is an emerging research sub area of Data Mining domain area. There are systems which can discover generalized topological patterns in a Big Graph using static Big Data set. In case of underlying Big Data is dynamic then generated Big Graph will change. There is need to develop a technique to discover the most relevant categorical, topological patterns in the new Big Graph. This paper highlights development of technique for discovering most relevant, categorical topological patterns in a Big Graph generated on online-time-series dynamic Big Data.