A landmark-model based system for mining frequent patterns from uncertain data streams

Abstract

Huge volumes of streaming data have been generated by sensors for applications such as environment surveillance. Partially due to the inherited limitation of sensors, these continuous streaming data can be uncertain. Over the past few years, algorithms have been proposed to apply the sliding window or time-fading window model to mine frequent patterns from streams of uncertain data. However, there are also other models to process data streams. In this paper, we propose a landmark-model based system for mining frequent patterns from streams of uncertain data.