A Multi-Domain Architecture for Mining Frequent Items and Itemsets from Distributed Data Streams

Real-time analysis of distributed data streams is a challenging task since it requires scalable solutions to handle streams of data that are generated very rapidly by multiple sources. This paper presents the design and the implementation of an architecture for the analysis of data streams in distributed environments. In particular, data stream analysis has been carried out for the computation of items and itemsets that exceed a frequency threshold. The mining approach is hybrid, that is, frequent items are calculated with a single pass, using a sketch algorithm, while frequent itemsets are calculated by a further multi-pass analysis. The architecture combines parallel and distributed processing to keep the pace with the rate of distributed data streams. In order to keep computation close to data, miners are distributed among the domains where data streams are generated. The paper reports the experimental results obtained with a prototype of the architecture, tested on a Grid composed of three domains each one handling a data stream. © 2013 Springer Science+Business Media Dordrecht.