Integrating textual data into heterogeneous data ingestion processing
In this abstract, two methods for integrating textual data and textual features into ingestion processing are summa- rized. The first method involves integrating all features, including textual features, into dedicated frameworks, such as by using ma- chine learning techniques. In the second method, text and textual features, such as keywords, are used to explain results returned by heterogeneous data mining. In this context, it is necessary to link data (e.g., databases, images, etc.) and/or obtained results with textual data (e.g., documents and keywords).
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| Main Authors: | , |
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| Format: | conference_item biblioteca |
| Language: | eng |
| Published: |
IEEE Computer Society
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| Online Access: | http://agritrop.cirad.fr/600139/ http://agritrop.cirad.fr/600139/2/Roche_Teisseire_BigData2021.pdf |
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| Summary: | In this abstract, two methods for integrating textual data and textual features into ingestion processing are summa- rized. The first method involves integrating all features, including textual features, into dedicated frameworks, such as by using ma- chine learning techniques. In the second method, text and textual features, such as keywords, are used to explain results returned by heterogeneous data mining. In this context, it is necessary to link data (e.g., databases, images, etc.) and/or obtained results with textual data (e.g., documents and keywords). |
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