@article{Nunes_Romani_Avila_Coltri_Traina_Sousa_2013, title={Finding Spatio-Temporal Patterns in Multidimensional Data Streams}, volume={4}, url={https://sol.sbc.org.br/journals/index.php/jidm/article/view/1501}, DOI={10.5753/jidm.2013.1501}, abstractNote={<div>In the last few decades, advances in data acquisition technology have contributed to generation of huge volumes of data in diverse application areas, creating new research challenges in knowledge discovery.</div><div>The analysis of these data has become an important task in several domains such as sensor networks, web-logs, financial transactions and climate change monitoring. In this article, we propose the Spatio-Temporal Behavior Meter (STB-meter) method to identify spatio-temporal patterns in multidimensional evolving data streams. Our approach combines a multi-resolution hierarchical structure to deal with spatial information with fractal-based analysis to monitor non spatial information of the multidimensional data stream. Experimental evaluation on real climate data shows that our method allows finding relevant spatio-temporal patterns in evolving data at different spatial and temporal resolutions and therefore it can be a useful tool to assist domain specialists in climate change researches.</div>}, number={3}, journal={Journal of Information and Data Management}, author={Nunes, Santiago A. and Romani, Luciana A. S. and Avila, Ana M. H. and Coltri, Priscila P. and Traina, Agma J. M. and Sousa, Elaine P. M.}, year={2013}, month={Sep.}, pages={327} }