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DATENZUSAMMENFÜGUNG UND -HARMONISIERUNG FÜR MASCHINELLES LERNEN
DATENZUSAMMENFÜGUNG UND -HARMONISIERUNG FÜR MASCHINELLES LERNEN
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摘要
Techniques are disclosed for automatically pre-processing data to generate a single view of the data that is suitable for machine learning and data analytics operations. Multiple data sets are joined together using one or more primary keys if raw data in the data sets have a same frequency. On the other hand, if raw data in the data sets do not have the same frequency, then for raw data in data sets having a different frequency than data in a user-specified base data set, the raw data is normalized and resampled. The normalized and resampled data in the data sets is further aggregated based on timestamps associated with the base data set, and the data sets are then joined to the base data set using one or more primary keys. The joined data sets can be stored and used to train machine learning models and/or for data analytics operations.
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