In this paper, we propose a new research problem on active learning from data streams where data volumes grow continuously. The objective is to label a small portion of stream data from which a model is derived to predict future instances as accurately as possible. We propose a classifier-ensemble based active learning framework which selectively labels instances from data streams to build an e...

Density estimation is an important but costly operation for applications that need to know the distribution of a data set. Moreover, when the data comes as a stream, traditional density estimation methods cannot cope with it efficiently. In this paper, we examined the problem of computing density function over data streams and developed a novel method to solve it. A new concept M-Kernel is used...

Big data is a ubiquitous feature of large modern enterprises. Many organizations generate huge amounts of on-line streaming data – examples include network monitoring, Twitter feeds, financial data, and industrial application monitoring. Making effective use of these data streams can be challenging. While Data Stream Management Systems can provide support for realtime alerting and data reductio...