https://sdq.kastel.kit.edu/index.php?title=Efficient_k-NN_Search_of_Time_Series_in_Arbitrary_Time_Intervals&feed=atom&action=historyEfficient k-NN Search of Time Series in Arbitrary Time Intervals - Versionsgeschichte2024-03-29T13:22:32ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Efficient_k-NN_Search_of_Time_Series_in_Arbitrary_Time_Intervals&diff=557&oldid=prevJens.willkomm@kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Janek Bettinger |email=janek.bettinger@student.kit.edu |vortragstyp=Masterarbeit |betreuer=Jens Willkomm |termin=Institutsseminar/2018-…“2018-03-15T15:03:04Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Janek Bettinger |email=janek.bettinger@student.kit.edu |vortragstyp=Masterarbeit |betreuer=Jens Willkomm |termin=Institutsseminar/2018-…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Janek Bettinger<br />
|email=janek.bettinger@student.kit.edu<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Jens Willkomm<br />
|termin=Institutsseminar/2018-03-23<br />
|kurzfassung=The k nearest neighbors (k-NN) of a time series are the k closest sequences within a<br />
dataset regarding a distance measure. Often, not the entire time series, but only specific<br />
time intervals are of interest, e.g., to examine phenomena around special events. While<br />
numerous indexing techniques support the k-NN search of time series, none of them<br />
is designed for an efficient interval-based search. This work presents the novel index<br />
structure Time Series Envelopes Index Tree (TSEIT), that significantly speeds up the k-NN<br />
search of time series in arbitrary user-defined time intervals.<br />
}}</div>Jens.willkomm@kit.edu