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Effective and scalable data discovery with NextiaJD
dc.contributor.author | Flores Herrera, Javier de Jesús |
dc.contributor.author | Nadal Francesch, Sergi |
dc.contributor.author | Romero Moral, Óscar |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Computació |
dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat Erasmus Mundus en Tecnologies de la Informació per a la Intel·ligència Empresarial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació |
dc.date.accessioned | 2021-04-06T10:11:42Z |
dc.date.available | 2021-04-06T10:11:42Z |
dc.date.issued | 2021 |
dc.identifier.citation | Flores, J.; Nadal, S.; Romero, O. Effective and scalable data discovery with NextiaJD. A: International Conference on Extending Database Technology. "Advances in Database Technology: EDBT 2021, 24th International Conference on Extending Database Technology: Nicosia, Cyprus, March 23-26, 2021: proceedings". Konstanz: OpenProceedings, 2021, p. 690-693. ISBN 978-3-89318-084-4. DOI 10.5441/002/edbt.2021.85. |
dc.identifier.isbn | 978-3-89318-084-4 |
dc.identifier.uri | http://hdl.handle.net/2117/343152 |
dc.description.abstract | We present NextiaJD, a data discovery system with high predictive performance and computational efficiency. NextiaJD aids data scientists in the discovery of datasets that can be crossed. To that end, it proposes a ranking of candidate pairs according to their join quality, which is based on a novel similarity measure that considers both containment and cardinality pro- portions between candidate attributes. To do so, NextiaJD adopts a learning approach relying on profiles. These are succint and informative representations of the schemata and data values of datasets that capture their underlying characteristics. NextiaJD's features are fully integrated into Apache Spark and benefits from it to parallelize the profiling and discovery processes. The on-site demonstration will showcase how NextiaJD can effectively support large-scale data discovery tasks with a large set of datasets the audience will be able to play with. |
dc.description.sponsorship | This work is partly supported by Barcelona’s City Council under grant agreement 20S08704. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico). |
dc.format.extent | 4 p. |
dc.language.iso | eng |
dc.publisher | OpenProceedings |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació |
dc.subject.lcsh | Data sets |
dc.subject.lcsh | Big data |
dc.subject.lcsh | Data mining |
dc.title | Effective and scalable data discovery with NextiaJD |
dc.type | Conference lecture |
dc.subject.lemac | Conjunts de dades |
dc.subject.lemac | Dades massives |
dc.subject.lemac | Mineria de dades |
dc.contributor.group | Universitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering |
dc.identifier.doi | 10.5441/002/edbt.2021.85 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://doi.org/10.5441/002/edbt.2021.85 |
dc.rights.access | Open Access |
local.identifier.drac | 30818377 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/Ajuntament de Barcelona/20S08704 |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/V PRI/2020 DI 027 |
local.citation.author | Flores, J.; Nadal, S.; Romero, O. |
local.citation.contributor | International Conference on Extending Database Technology |
local.citation.pubplace | Konstanz |
local.citation.publicationName | Advances in Database Technology: EDBT 2021, 24th International Conference on Extending Database Technology: Nicosia, Cyprus, March 23-26, 2021: proceedings |
local.citation.startingPage | 690 |
local.citation.endingPage | 693 |