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Abstract. We propose an integrated approach for solving both problems of finding the most popular k elements, and finding frequent elements in a data stream. Our technique is efficient and exact if the alphabet under consideration is small. In the more practical large alphabet case, our solution is space efficient and reports both top-k and frequent elements with tight guarantees on errors. For general data distributions, our top-k algorithm can return a set of k elements, where k ≈ k, which are guaranteed to be the top-k' elements; and we use minimal space for calculating frequent elements. For realistic Zipfian data, our space requirement for the frequent elements problem decreases dramatically with the parameter of the distribution; and for top-k queries, we ensure that only the top-k elements, in the correct order, are reported. Our experiments show significant space reductions with no loss in accuracy.

Abstract. Publish/Subscribe systems have become a prevalent model for delivering data from producers (publishers) to consumers (subscribers) distributed across wide-area networks while decoupling the publishers and the subscribers from each other. In this paper we present Meghdoot, which adapts content-based publish/subscribe systems to Distributed Hash Table based P2P networks in order to provide scalable content delivery mechanisms while maintaining the decoupling between the publishers and the subscribers. Meghdoot is designed to adapt to highly skewed data sets, which is typical of real applications. The experimental results demonstrate that Meghdoot balances the load among the peers and the design scales well with increasing number of peers, subscriptions and events.

Peer-To-Peer (P2P) networks are self-organizing, distributed systems, with no centralized authority or infrastructure. Because of the voluntary participation, the availability of resources in a P2P system can be highly variable and unpredictable. In this paper, we use ideas from Game Theory to study the interaction of strategic and rational peers, and propose a differential service-based incentive scheme to improve the system's performance.

We propose an approximate integrated approach for solving both problems of finding the most popular k elements, and finding frequent elements in a data stream coming from a large domain. Our solution is space efficient and reports both frequent and top- k elements with tight guarantees on errors. For general data distributions, our top- k algorithm returns k elements that have roughly the highest frequencies; and it uses limited space for calculating frequent elements. For realistic Zipfian data, the space requirement of the proposed algorithm for solving the exact frequent elements problem decreases dramatically with the parameter of the distribution; and for top- k queries, the analysis ensures that only the top- k elements, in the correct order, are reported. The experiments, using real and synthetic data sets, show space reductions with hardly any loss in accuracy. Having proved the effectiveness of the proposed approach through both analysis and experiments, we extend it to be able to answer continuous queries about frequent and top- k elements. Although the problems of incremental reporting of frequent and top- k elements are useful in many applications, to the best of our knowledge, no solution has been proposed.