G-skyline query over data stream in wireless sensor network

摘要

There are much data sampled continuously by sensors in the wireless sensor network. Storing and mining these data can find more potential information and provide help for decision making. As an important technology for data mining and multi-criteria decision, skyline computation can identify the interesting single points for user. In order to analyze the groups of points, the group-based skyline is proposed to query all the Pareto Optimal groups which are not g-dominated by other groups with the same number of points. Existing algorithms about g-skyline can just compute static data. However, data stream is very common in many applications, and it is very important to design algorithm go query g-skyline over data stream. In this paper, we propose new algorithms to compute g-skyline over a data stream. We present sharing strategy and then present two efficient algorithms: point-arriving algorithm and point-expiring algorithm. The experimental results on three kinds of synthetic data and a real stock data show that our algorithms perform efficiently over a data stream.