Abstract

The challenge of deriving insights from big data has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. This tutorial is a gentle introduction to mining big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part discusses data stream mining on distributed engines such as Storm, S4, and Samza