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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dig-cirad-fr-6001392023-10-27T07:46:18Z http://agritrop.cirad.fr/600139/ http://agritrop.cirad.fr/600139/ Integrating textual data into heterogeneous data ingestion processing. Roche Mathieu, Teisseire Maguelonne. 2021. In : 2021 IEEE International Conference on Big Data. IEEE. Orlando : IEEE Computer Society, 6008-6010. ISBN 978-1-6654-3902-2 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, États-Unis, 15 Décembre 2021/18 Décembre 2021.https://doi.org/10.1109/BigData52589.2021.9671759 <https://doi.org/10.1109/BigData52589.2021.9671759> Integrating textual data into heterogeneous data ingestion processing Roche, Mathieu Teisseire, Maguelonne eng 2021 IEEE Computer Society 2021 IEEE International Conference on Big Data 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). conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/acceptedVersion http://agritrop.cirad.fr/600139/2/Roche_Teisseire_BigData2021.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1109/BigData52589.2021.9671759 10.1109/BigData52589.2021.9671759 info:eu-repo/semantics/altIdentifier/doi/10.1109/BigData52589.2021.9671759 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1109/BigData52589.2021.9671759 |
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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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Roche, Mathieu Teisseire, Maguelonne |
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Roche, Mathieu Teisseire, Maguelonne Integrating textual data into heterogeneous data ingestion processing |
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Roche, Mathieu Teisseire, Maguelonne |
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Roche, Mathieu |
title |
Integrating textual data into heterogeneous data ingestion processing |
title_short |
Integrating textual data into heterogeneous data ingestion processing |
title_full |
Integrating textual data into heterogeneous data ingestion processing |
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Integrating textual data into heterogeneous data ingestion processing |
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Integrating textual data into heterogeneous data ingestion processing |
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integrating textual data into heterogeneous data ingestion processing |
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IEEE Computer Society |
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http://agritrop.cirad.fr/600139/ http://agritrop.cirad.fr/600139/2/Roche_Teisseire_BigData2021.pdf |
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AT rochemathieu integratingtextualdataintoheterogeneousdataingestionprocessing AT teisseiremaguelonne integratingtextualdataintoheterogeneousdataingestionprocessing |
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1781883499195662336 |