Distributed Monitoring of Frequent Items

Monitoring frequently occuring items is a recurring task in a variety of applications. Although a number of solutions have been proposed there has been few to address the problem in a distributed networked environment. Most past solutions relied upon approximating results to lower communication overhead. In this paper we introduce a new algorithm designed for continuously tracking frequent items over distributed data streams providing either exact or approximate answers. We tested the efficiency of our method using two real-world data sets. The results indicated significant reduction in communication cost when compared to na

• Type: Journal
• Year: 2008
• Where: MLDM
• Authors: Robert Fuller, Mehmed M. Kantardzic
• Keywords: Frequent Items, Machine Learning, MLDM 2008, Most Past Solutions, Real-world Data Sets