  A growing number of applications that generate massive streams of data need
intelligent data processing and online analysis. Real-time surveillance
systems, telecommunication systems, sensor networks and other dynamic
environments are such examples. The imminent need for turning such data into
useful information and knowledge augments the development of systems,
algorithms and frameworks that address streaming challenges. The storage,
querying and mining of such data sets are highly computationally challenging
tasks. Mining data streams is concerned with extracting knowledge structures
represented in models and patterns in non stopping streams of information.
Generally, two main challenges are designing fast mining methods for data
streams and need to promptly detect changing concepts and data distribution
because of highly dynamic nature of data streams. The goal of this article is
to analyze and classify the application of diverse data mining techniques in
different challenges of data stream mining. In this paper, we present the
theoretical foundations of data stream analysis and propose an analytical
framework for data stream mining techniques.
