Top-κ Item identification on dynamic and distributed datasets

The problem of identifying the most frequent items across multiple datasets has received considerable attention over the last few years. When storage is a scarce resource, the topic is already a challenge; yet, its complexity may be further exacerbated not only by the many independent data sources, but also by the dynamism of the data, i.e., the fact that new items may appear and old ones disappear at any time. In this work, we provide a novel approach to the problem by using an existing gossip-based algorithm for identifying the k most frequent items over a distributed collection of datasets, in ways that deal with the dynamic nature of the data. The algorithm has been thoroughly analyzed through trace-based simulations and compared to state-of-the-art decentralized solutions, showing better precision at reduced communication overhead.