Estimating Missing Data in Data Streams

Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and tracking. Many research projects have been conducted by different organizations regarding wireless sensor networks; however, few of them discuss how to estimate missing sensor data. In this research we present a novel data estimation technique based on association rules derived from closed frequent itemsets generated by sensors. Experimental results compared with the existing techniques using real-life sensor data show that closed itemset mining effectively imputes missing values as well as achieves time and space efficiency.

• Type: Conference
• Year: 2007
• Where: DASFAA
• Authors: Nan Jiang, Le Gruenwald
• Keywords: DASFAA 2007, Database, Real-life Sensor Data, Sensor Data, Wireless Sensor Networks