Feature Engineering Techniques and Spatio-Temporal Data Processing

dc.contributor.author	Forke, Chris-Marian
dc.contributor.author	Tropmann-Frick, Marina
dc.date.accessioned	2022-01-27T13:27:55Z
dc.date.available	2022-01-27T13:27:55Z
dc.date.issued	2021
dc.description.abstract	More and more applications nowadays use spatio-temporal data for different purposes. In order to be processed and used efficiently, this unique type of data requires special handling. This paper summarizes methods and approaches for feature selection of spatio-temporal data and machine learning algorithms for spatio-temporal data engineering. Furthermore, it highlights relevant work in specific domains. The range of possible approaches for data processing is quite wide. However, in order to use these approaches with the spatio-temporal data in a meaningful and practical way, individual data processing steps need to be adapted. One of the most important steps is feature engineering.	de
dc.identifier.doi	10.1007/s13222-021-00391-x
dc.identifier.pissn	1610-1995
dc.identifier.uri	http://dx.doi.org/10.1007/s13222-021-00391-x
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/38052
dc.publisher	Springer
dc.relation.ispartof	Datenbank-Spektrum: Vol. 21, No. 3
dc.relation.ispartofseries	Datenbank-Spektrum
dc.subject	Feature engineering
dc.subject	Feature selection
dc.subject	Spatio-temporal data
dc.title	Feature Engineering Techniques and Spatio-Temporal Data Processing	de
dc.type	Text/Journal Article
gi.citation.endPage	244
gi.citation.startPage	237

Sammlungen