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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29574
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dc.contributor.authorSpillner, Josef-
dc.date.accessioned2024-01-12T14:46:29Z-
dc.date.available2024-01-12T14:46:29Z-
dc.date.issued2022-06-22-
dc.identifier.isbn978-1-6654-6847-3de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29574-
dc.description.abstractAI 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.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectData intelligencede_CH
dc.subjectBeyond-schema inferencede_CH
dc.subjectPattern recognitionde_CH
dc.subjectMLOpsde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleTabular data insights and synthesis with the AutoTable approachde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/SDS54800.2022.00020de_CH
dc.identifier.doi10.21256/zhaw-29574-
zhaw.conference.details9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end70de_CH
zhaw.pages.start69de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings 2022 9th Swiss Conference on Data Science (SDS)de_CH
zhaw.webfeedService Engineeringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.relation.referenceshttps://github.com/serviceprototypinglab/autotablede_CH
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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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