Frequent Items in Streaming Data: An Experimental Evaluation of the State-of-the-Art

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 a variety of real and synthetic data. 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. This work has been published in the Data and Knowledge Engineering (DKE) journal. Please reference it as follows: Nishad Manerikar, Themis Palpanas. Frequent Items in Streaming Data: An Experimental Evaluation of the State-of-the-Art. Data and Knowledge Engineering (DKE) 68(4), 2009: 415-430