Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Datenbanksysteme für Business, Technologie und Web (BTW 2021) |
| Untertitel | 19. Fachtagung des GI-Fachbereichs ,,Datenbanken und Informationssysteme" (DBIS), 13.-17. September 2021, Dresden, Germany, Proceedings |
| Herausgeber/-innen | Kai-Uwe Sattler, Melanie Herschel, Wolfgang Lehner |
| Herausgeber (Verlag) | Gesellschaft fur Informatik (GI) |
| Seiten | 313-324 |
| Seitenumfang | 12 |
| ISBN (elektronisch) | 978-3-88579-705-0 |
| Publikationsstatus | Veröffentlicht - 2021 |
Publikationsreihe
| Name | Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) |
|---|---|
| Band | P-311 |
| ISSN (Print) | 1617-5468 |
Abstract
Data transformation discovery is one of the most tedious tasks in data preparation. In particular, the generation of transformation programs for semantic transformations is tricky because additional sources for look-up operations are necessary. Current systems for semantic transformation discovery face two major problems: either they follow a program synthesis approach that only scales to a small set of input tables, or they rely on extraction of transformation functions from large corpora, which requires the identification of exact transformations in those resources and is prone to noisy data. In this paper, we try to combine approaches to benefit from large corpora and the sophistication of program synthesis. To do so, we devise a retrieval and pruning strategy ensemble that extracts the most relevant tables for a given transformation task. The extracted resources can then be processed by a program synthesis engine to generate more accurate transformation results than state-of-the-art.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
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Datenbanksysteme für Business, Technologie und Web (BTW 2021): 19. Fachtagung des GI-Fachbereichs ,,Datenbanken und Informationssysteme" (DBIS), 13.-17. September 2021, Dresden, Germany, Proceedings. Hrsg. / Kai-Uwe Sattler; Melanie Herschel; Wolfgang Lehner. Gesellschaft fur Informatik (GI), 2021. S. 313-324 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Band P-311).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Combining Programming-by-Example with Transformation Discovery from large Databases
AU - Özmen, Aslihan
AU - Esmailoghli, Mahdi
AU - Abedjan, Ziawasch
N1 - Funding information:. This project has been supported by the German Research Foundation (DFG) under grant agreement 387872445.
PY - 2021
Y1 - 2021
N2 - Data transformation discovery is one of the most tedious tasks in data preparation. In particular, the generation of transformation programs for semantic transformations is tricky because additional sources for look-up operations are necessary. Current systems for semantic transformation discovery face two major problems: either they follow a program synthesis approach that only scales to a small set of input tables, or they rely on extraction of transformation functions from large corpora, which requires the identification of exact transformations in those resources and is prone to noisy data. In this paper, we try to combine approaches to benefit from large corpora and the sophistication of program synthesis. To do so, we devise a retrieval and pruning strategy ensemble that extracts the most relevant tables for a given transformation task. The extracted resources can then be processed by a program synthesis engine to generate more accurate transformation results than state-of-the-art.
AB - Data transformation discovery is one of the most tedious tasks in data preparation. In particular, the generation of transformation programs for semantic transformations is tricky because additional sources for look-up operations are necessary. Current systems for semantic transformation discovery face two major problems: either they follow a program synthesis approach that only scales to a small set of input tables, or they rely on extraction of transformation functions from large corpora, which requires the identification of exact transformations in those resources and is prone to noisy data. In this paper, we try to combine approaches to benefit from large corpora and the sophistication of program synthesis. To do so, we devise a retrieval and pruning strategy ensemble that extracts the most relevant tables for a given transformation task. The extracted resources can then be processed by a program synthesis engine to generate more accurate transformation results than state-of-the-art.
UR - http://www.scopus.com/inward/record.url?scp=85130137666&partnerID=8YFLogxK
U2 - 10.18420/BTW2021-16
DO - 10.18420/BTW2021-16
M3 - Conference contribution
T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
SP - 313
EP - 324
BT - Datenbanksysteme für Business, Technologie und Web (BTW 2021)
A2 - Sattler, Kai-Uwe
A2 - Herschel, Melanie
A2 - Lehner, Wolfgang
PB - Gesellschaft fur Informatik (GI)
ER -