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dc.rights.licenseCC BY 4.0 Unportedeng
dc.contributor.authorEndris, Kemele M.-
dc.contributor.authorVidal, Maria-Esther-
dc.contributor.authorGraux, Damien-
dc.contributor.editorJanev, Valentina-
dc.contributor.editorGraux, Damien-
dc.contributor.editorJabeen, Hajira-
dc.contributor.editorSallinger, Emanuel-
dc.date.accessioned2021-03-18T15:42:12Z-
dc.date.available2021-03-18T15:42:12Z-
dc.date.issued2020-
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6097-
dc.identifier.urihttps://doi.org/10.34657/5079-
dc.description.abstractBig data plays a relevant role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Semantic web technologies have also experienced great progress, and scientific communities and practitioners have contributed to the problem of big data management with ontological models, controlled vocabularies, linked datasets, data models, query languages, as well as tools for transforming big data into knowledge from which decisions can be made. Despite the significant impact of big data and semantic web technologies, we are entering into a new era where domains like genomics are projected to grow very rapidly in the next decade. In this next era, integrating big data demands novel and scalable tools for enabling not only big data ingestion and curation but also efficient large-scale exploration and discovery. Federated query processing techniques provide a solution to scale up to large volumes of data distributed across multiple data sources. Federated query processing techniques resort to source descriptions to identify relevant data sources for a query, as well as to find efficient execution plans that minimize the total execution time of a query and maximize the completeness of the answers. This chapter summarizes the main characteristics of a federated query engine, reviews the current state of the field, and outlines the problems that still remain open and represent grand challenges for the area.eng
dc.language.isoengeng
dc.publisherCham : Springereng
dc.relation.ispartofKnowledge Graphs and Big Data Processingeng
dc.relation.ispartofseriesLecture Notes in Computer Scienceeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subjectLAMBDA Projecteng
dc.subjectBig Dataeng
dc.subjectQuery Engineeng
dc.subject.ddc004eng
dc.titleFederated Query Processingeng
dc.typebookParteng
dc.typeTexteng
dc.description.versionpublishedVersioneng
wgl.contributorTIBeng
wgl.subjectInformatikeng
wgl.typeBuchkapitel / Sammelwerksbeitrageng
dc.bibliographicCitation.firstPage73eng
dc.bibliographicCitation.lastPage86eng
dc.bibliographicCitation.volume12072eng
dc.relation.doihttps://doi.org/10.1007/978-3-030-53199-7_5-
dc.relation.essn1611-3349-
dc.relation.issn0302-9743-
dc.relation.isbn978-3-030-53198-0-
dcterms.bibliographicCitation.journalTitleLecture Notes in Computer Scienceeng
tib.accessRightsopenAccesseng
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Endris, Kemele M., Maria-Esther Vidal and Damien Graux, 2020. Federated Query Processing. In: (Hrsg.)Valentina Janev, Damien Graux, Hajira Jabeen and Emanuel Sallinger. Cham : Springer. ISBN 978-3-030-53198-0
Endris, K. M., Vidal, M.-E. and Graux, D. (2020) “Federated Query Processing.” Cham : Springer. doi: https://doi.org/10.1007/978-3-030-53199-7_5.
Endris K M, Vidal M-E, Graux D. Federated Query Processing. In: , editorJanev V, Graux D, Jabeen H, Sallinger E. Cham : Springer; 2020.
Endris, K. M., Vidal, M.-E., & Graux, D. (2020). Federated Query Processing. Cham : Springer. https://doi.org/https://doi.org/10.1007/978-3-030-53199-7_5
Endris K M, Vidal M-E, Graux D. Federated Query Processing. In: , ed.Janev V, Graux D, Jabeen H, Sallinger E Vol. 12072. Cham : Springer; 2020. doi:https://doi.org/10.1007/978-3-030-53199-7_5


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