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 naive approaches and an existing efficient algorithm called Top-K Monitoring. Since our method does not rely upon approximations to reduce communication overhead and is explicitly designed for tracking frequent items, our method also shows increased quality in its tracking results.