A PWF smoothing algorithm for K-sensitive stream mining technologies over sliding windows

Abstract

The development of Streaming Mining technologies as a hotspot entered the limelight, which is more effectively to avoid big data and distributed streams mining problems. Especially for the IoT and Ubiquitous Computing may interact with the real world’s humans and physical objects in a sensory manner. They require quantitative guarantees regarding the precision of approximate answers and support distributed processing of high-volume, fast, and variety streams. Recent works on mining Top-k synopsis processing over data streams is that utilize all the data between a particular point of landmark and the current time for mining. Actually, the landmark and parameter k are two more important factors to obtain high-quality approximate results. Therefore, we proposed a Proper-Wavelet Function (PWF) algorithm to smooth the approximate approach, in order to reduce k-effect to the final approximate results. Finally, we demonstrate the effectiveness of our algorithm in achieving high-quality k-nearest neighbors mining results with applying wider proper k values.