A spatio-temporal data stream is a sequence of time-stamped geo-referenced data elements which arrive at consecutive time points. In addition to the spatial and temporal dimensions which are information bearing, stream poses further challenges to data mining, which are avoiding multiple scans of the entire data sets, optimizing memory usage, and mining only the most recent patterns. In this paper, we address the challenges of mining spatiotemporal data streams for a new class of space-time patterns, called trend-clusters. These patterns combine spatial clustering and trend discovery in stream environments. In particular, we propose a novel algorithm, called TRUST, which allows to retrieve groups of spatially continuous geo-referenced data which variate according to a close trend polyline in the recent window past. Experiments demonstrate the effectiveness of the proposed algorithm.
Clustering Spatio-Temporal Data Streams
APPICE, ANNALISA;MALERBA, Donato;
2010-01-01
Abstract
A spatio-temporal data stream is a sequence of time-stamped geo-referenced data elements which arrive at consecutive time points. In addition to the spatial and temporal dimensions which are information bearing, stream poses further challenges to data mining, which are avoiding multiple scans of the entire data sets, optimizing memory usage, and mining only the most recent patterns. In this paper, we address the challenges of mining spatiotemporal data streams for a new class of space-time patterns, called trend-clusters. These patterns combine spatial clustering and trend discovery in stream environments. In particular, we propose a novel algorithm, called TRUST, which allows to retrieve groups of spatially continuous geo-referenced data which variate according to a close trend polyline in the recent window past. Experiments demonstrate the effectiveness of the proposed algorithm.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
