A Quick Survey on Data Stream Mining

Abstract — Traditional databases store sets of relatively static records without the concept of time, unless timestamp attributes are explicitly added. Limitations of traditional DBMSs in supporting streaming applications have been understood, prompting research to supplement existing technologies and build new systems to manage streaming data. At present a growing number of applications that generate massive streams of data need intelligent data processing and online analysis. The impending need for turning such data into useful information and knowledge augments the development of algorithms and frameworks that address streaming challenges. The pre processing, storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams implies extracting knowledge structures represented in models and patterns in non stopping streams of information. In this paper, we present the theoretical foundations of data stream analysis and identify potential stream mining techniques. Keywords—Data streams, Data mining