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https://doi.org/10.21256/zhaw-29574| Publikationstyp: | Konferenz: Poster |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Tabular data insights and synthesis with the AutoTable approach |
| Autor/-in: | Spillner, Josef |
| et. al: | No |
| DOI: | 10.1109/SDS54800.2022.00020 10.21256/zhaw-29574 |
| Tagungsband: | Proceedings 2022 9th Swiss Conference on Data Science (SDS) |
| Seite(n): | 69 |
| Seiten bis: | 70 |
| Angaben zur Konferenz: | 9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022 |
| Erscheinungsdatum: | 22-Jun-2022 |
| Verlag / Hrsg. Institution: | IEEE |
| ISBN: | 978-1-6654-6847-3 |
| Sprache: | Englisch |
| Schlagwörter: | Data intelligence; Beyond-schema inference; Pattern recognition; MLOps |
| Fachgebiet (DDC): | 005: Computerprogrammierung, Programme und Daten |
| Zusammenfassung: | AI convergence platforms such as Google's Unified AI Platform promise to fully interpret and understand any data submitted to them. The business needs of SMEs are however better addressed by tailored tools that smartly parse and interpret data without being locked into a particular vendor platform. With AutoTable, a new tool design for schema, pattern and relation inference as well as training data synthesis has recently become available. This paper explains why AutoTable is smart, unintrusive and yet powerful in working with tabular business data such as CSVs, flat JSON and spreadsheets. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29574 |
| Zugehörige Forschungsdaten: | https://github.com/serviceprototypinglab/autotable |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Spillner_Tabular-data-insights-synthesis-AutoTable-approach_IEEE_Poster.pdf | Accepted Version | 117.05 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Spillner, J. (2022). Tabular data insights and synthesis with the AutoTable approach [Conference poster]. Proceedings 2022 9th Swiss Conference on Data Science (SDS), 69–70. https://doi.org/10.1109/SDS54800.2022.00020
Spillner, J. (2022) ‘Tabular data insights and synthesis with the AutoTable approach’, in Proceedings 2022 9th Swiss Conference on Data Science (SDS). IEEE, pp. 69–70. Available at: https://doi.org/10.1109/SDS54800.2022.00020.
J. Spillner, “Tabular data insights and synthesis with the AutoTable approach,” in Proceedings 2022 9th Swiss Conference on Data Science (SDS), Jun. 2022, pp. 69–70. doi: 10.1109/SDS54800.2022.00020.
SPILLNER, Josef, 2022. Tabular data insights and synthesis with the AutoTable approach. In: Proceedings 2022 9th Swiss Conference on Data Science (SDS). Conference poster. IEEE. 22 Juni 2022. S. 69–70. ISBN 978-1-6654-6847-3
Spillner, Josef. 2022. “Tabular Data Insights and Synthesis with the AutoTable Approach.” Conference poster. In Proceedings 2022 9th Swiss Conference on Data Science (SDS), 69–70. IEEE. https://doi.org/10.1109/SDS54800.2022.00020.
Spillner, Josef. “Tabular Data Insights and Synthesis with the AutoTable Approach.” Proceedings 2022 9th Swiss Conference on Data Science (SDS), IEEE, 2022, pp. 69–70, https://doi.org/10.1109/SDS54800.2022.00020.
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