Breve descrizione dei contenuti: The problem of detecting frequent items in streaming data is relevant to many different applications across
many domains. Several algorithms, diverse in nature, have been proposed in the literature for the solution
of the above problem. In this paper, we review these algorithms, and we present the results of the first
extensive comparative experimental study of the most prominent algorithms in the literature. The algorithms
were comprehensively tested using a common test framework on several real and synthetic datasets. Their
performance with respect to the different parameters (i.e., parameters intrinsic to the algorithms, and data
related parameters) was studied. We report the results, and insights gained through these experiments.