Recently a new class of data-intensive application has become widely used in which the data is modeled not as persistent relations but as data streams. Examples of such applications include financial applications, network monitoring, security, telecommunications data management, web applications, manufacturing, sensor networks, and others. As a consequence, there has been a dramatically increasing amount of interest in querying and mining such data which in turn resulted in a large amount of work introducing new methodologies for indexing, classification and approximation of time series. Research in this field has focused on the development of effective transformation techniques, the application of dimensionality reduction methods and the design of efficient indexing schemes. Traditional access methods that continuously update data are considered inappropriate, due to significant update costs. The proposed method called as adaptive stream processing is based on an incremental computation of Discrete wavelet transform which is used as a feature extraction method and efficient technique for similarity query processing using sliding windows In order to prove the efficiency of the proposed method, experiments have been performed for range query and k-nearest neighbor query on real-life data sets. The results have shown that the adaptive stream processing method exhibit consistently better performance in comparison to previously proposed approaches.