I-HASTREAM: Density-Based Hierarchical Clustering of Big Data Streams and Its Application to Big Graph Analytics Tools

Abstract

Big Data Streams are very popular at now, as stirred-up by a plethora of modern applications such as sensor networks, scientific computing tools, Web intelligence, social network analysis and mining tools, and so forth. Here, the main research issue consists in how to effectively and efficiently extract useful knowledge from (streaming) big data, in order to support innovative big data analytics platforms. To this end, clustering analysis is a well-known tool for extracting knowledge from big data streams, as also confirmed by recent trends in active literature. A special applicative case is represented by so-called graph-shaped data (big) streams, which are produced by graph sources providing both structure- and content-oriented knowledge. On top of such sources, big graph analytics is a leading scientific area to be considered. At the convergence of these emerging topics, in this paper we provide the following contributions: (i) I-HASTREAM, a novel density-based hierarchical clustering algorithm for evolving big data streams that founds on it predecessor, namely HASTREAM; (ii) the architecture of a big graph analytics engine that embeds I-HASTREAM in its core layer.