01588nam a2200253 a 450000100080000000500110000800800410001910000170006024500970007726001500017430000140032450000150033852007760035365000210112965000140115065000230116465000260118765000100121365300260122365300190124965300220126870000210129070000230131120329742020-01-21       2015    bl uuuu     u00u1 u    #d1 aBONES, C. C.  aClustering multivariate climate data streams using fractal dimension.h[electronic resource]  aIn: BRAZILIAN SYMPOSIUM ON DATABASES, 30., 2015, Petrópolis. Proceedings... Petrópolis: Laboratório Nacional de Computação Científicac2015  ap. 41-52.  aSBBD 2015.  aAbstract. A data stream is a flow of data produced continuously along the time. Storing and analyzing such information become challenging due to exponential growth of the data volume collected. In this context, some methods were proposed to cluster data streams with similar behavior along the time. However, those methods have failed on clustering data flows with more than one attribute, i.e., multivariate flows. This paper introduces a new method to cluster multivariate data streams, based on fractal dimension, reading the data only once. We evaluated our method over real multivariate data streams generated by climate sensors. Not only was our method able to cluster the flows of data, but also identified sensors with similar behavior during the analyzed period.  aCluster analysis  aDatabases  aFractal dimensions  aMultivariate analysis  aClima  aAnálise multivariada  aBanco de dados  aDimensão fractal1 aROMANI, L. A. S.1 aSOUSA, E. P. M. de