Change Detection in High Dimensional Data Streams: Unterschied zwischen den Versionen

Version vom 9. April 2021, 14:56 Uhr

Vortragende(r)	Tanja Fenn
Vortragstyp	Proposal
Betreuer(in)	Edouard Fouché
Termin	Fr 16. April 2021
Vortragsmodus
Kurzfassung	Data streams in real-world scenarios such as environmental analysis, manufacturing, and e-commerce are high-dimensional and evolve over time. This will result in outdated models, or events of interest emerge, such as in predictive maintenance. Hence, it is crucial to detect change, i.e., concept drift, to design a reliable and adaptive system for streaming data. Nevertheless, most popular concept drift detection algorithms detect when a drift occurs (“when”) but can only be applied to univariate data streams, and neglect to examine in which dimensions the drift occurs (“where”). Change detection algorithms should act unsupervised and detect change as fast as possible. Beyond that, processing high-dimensional data evokes further challenges like those from the curse of dimensionality, or where a drift occurs. The goal of this Master thesis is the development and evaluation of an unsupervised framework which enables to detect “when” and “where” a drift occurs. We train an autoencoder and detect drift by applying ADWIN on the autoencoder’s reconstruction error.

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 |betreuer=Edouard Fouché
 |termin=Institutsseminar/2021-04-16
-|kurzfassung=TBD
+|kurzfassung=Data streams in real-world scenarios such as environmental analysis, manufacturing, and e-commerce are high-dimensional and evolve over time. This will result in outdated models, or events of interest emerge, such as in predictive maintenance. Hence, it is crucial to detect change, i.e., concept drift, to design a reliable and adaptive system for streaming data. Nevertheless, most popular concept drift detection algorithms detect when a drift occurs (“when”) but can only be applied to univariate data streams, and neglect to examine in which dimensions the drift occurs (“where”).
+Change detection algorithms should act unsupervised and detect change as fast as possible. Beyond that, processing high-dimensional data evokes further challenges like those from the curse of dimensionality, or where a drift occurs. The goal of this Master thesis is the development and evaluation of an unsupervised framework which enables to detect “when” and “where” a drift occurs. We train an autoencoder and detect drift by applying ADWIN on the autoencoder’s reconstruction error.
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