iSAX 2.0: Indexing and Mining One Billion Time SeriesAlessandro Camerra Themis Palpanas Jin Shieh Eamonn KeoghUniversity of Trento
a.camerra@studenti.unitn.it, themis@disi.unitn.euUniversity of California, Riverside
{shiehj, eamonn}@cs.ucr.eduAbstract—There is an increasingly pressing need, by several
applications in diverse domains, for developing techniques able
to index and mine very large collections of time series.
Examples of such applications come from astronomy, biology,
the web,
