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Due to the advancement of Technologies, many companies and organizations today have huge databases, that grow to a limit of millions of records per day. This data generation and storage has become faster than ever. This resulted in databases of unbounded growth. The data even changes over time. As a consequence, if the data cannot fit into memory, it may be necessary to sample a smaller training set. The data streams have recently emerged to address the problems of continuous data. The mining of such data streams is a unique opportunity and even challenging task. Mining with data streams is the process of extracting knowledge structures from continuous, rapid data records. Due to increased streaming information, data stream mining has attracted the research community. Analyzing data streams helps in applications like scientific applications, business and astronomy etc. In this paper, we present the overview of the growing field of Data Streams. We cover the theoretical basis needed for analyzing the streams of data. We discuss the various techniques used for mining data streams. The focus of this paper is to study the problems involved in mining data streams. Finally, we conclude with a brief discussion of the big open problems in the area.
Lakshmi et al. (Tue,) studied this question.