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.
|Titolo:||Frequent Items in Streaming Data: An experimental evaluation of the state-of-the-art|
|Autori:||N., Manerikar; Palpanas, Themistoklis|
|Titolo del volume contenente il saggio:||Proceedings of Hellenic Data Management Symposium (HDMS)|
|Luogo di edizione:||Amsterdam|
|Anno di pubblicazione:||2009|
|Codice identificativo Scopus:||2-s2.0-61449190724|
|Appare nelle tipologie:||04.1 Saggio in atti di convegno (Paper in proceedings)|