Mining frequent items and itemsets from distributed data streams for emergency detection and management

Sensor networks are an important technology for large-scale monitoring, that allow the collection of environmental measurement streaming data in remote areas. Such data constitute a valuable source of information to be exploited for better understanding natural phenomena. Moreover, in some cases streams of data must be analyzed in real time to provide information about trends, outlier values or regularities that must be signaled as soon as possible, to prevent emergencies or disasters (e.g., landslides, fires). For such a reason, 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. Experimental evaluation shows the efficiency and effectiveness of the approach.