Tabular data insights and synthesis with the AutoTable approach

Spillner, Josef

doi:10.1109/SDS54800.2022.00020

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29574

Publication type:	Conference poster
Type of review:	Peer review (publication)
Title:	Tabular data insights and synthesis with the AutoTable approach
Authors:	Spillner, Josef
et. al:	No
DOI:	10.1109/SDS54800.2022.00020 10.21256/zhaw-29574
Proceedings:	Proceedings 2022 9th Swiss Conference on Data Science (SDS)
Page(s):	69
Pages to:	70
Conference details:	9th Swiss Conference on Data Science (SDS), Lucerne, Switzerland, 22-23 June 2022
Issue Date:	22-Jun-2022
Publisher / Ed. Institution:	IEEE
ISBN:	978-1-6654-6847-3
Language:	English
Subjects:	Data intelligence; Beyond-schema inference; Pattern recognition; MLOps
Subject (DDC):	005: Computer programming, programs and data
Abstract:	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
Related research data:	https://github.com/serviceprototypinglab/autotable
Fulltext version:	Accepted version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Computer Science (InIT)
Appears in collections:	Publikationen School of Engineering

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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.