Development and evaluation of efficient kNN search of time series subsequences using the example of the Google Ngram data set: Unterschied zwischen den Versionen
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{{Vortrag | |||
|vortragender=Janek Bettinger | |||
|email=nobody@example.com | |||
|vortragstyp=Proposal | |||
|betreuer=Jens Willkomm | |||
|termin=Institutsseminar/2017-08-18 | |||
|kurzfassung=There are many data structures and indices that speed up kNN queries on time series. The existing indices are designed to work on the full time series only. In this thesis we develop a data structure that allows speeding up kNN queries in an arbitrary time range, i.e. for an arbitrary subsequence. | |||
}} | |||
Aktuelle Version vom 27. September 2023, 14:50 Uhr
| Vortragende(r) | Janek Bettinger | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Jens Willkomm | |
| Termin | Fr 18. August 2017 | |
| Vortragsmodus | ||
| Kurzfassung | There are many data structures and indices that speed up kNN queries on time series. The existing indices are designed to work on the full time series only. In this thesis we develop a data structure that allows speeding up kNN queries in an arbitrary time range, i.e. for an arbitrary subsequence. | |