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dc.contributor.author
Polyzotis, Neoklis
dc.contributor.author
Rekatsinas, Theodoras
dc.contributor.author
Roy, Sudeepa
dc.contributor.author
Vartak, Manasi
dc.contributor.author
Zhang, Ce
dc.date.accessioned
2020-01-30T10:26:32Z
dc.date.available
2020-01-11T03:19:13Z
dc.date.available
2020-01-30T10:26:32Z
dc.date.issued
2019-08-01
dc.identifier.issn
2150-8097
dc.identifier.other
10.14778/3352063.3352149
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/389485
dc.identifier.doi
10.3929/ethz-b-000389485
dc.description.abstract
AI/ML is becoming a horizontal technology: its application is expanding to more domains, and its integration touches more parts of the technology stack. Given the strong dependence of ML on data, this expansion creates a new space for applying data management techniques. At the same time, the deeper integration of ML in the technology stack provides more touch points where ML can be used in data management systems and vice versa. In this panel, we invite researchers working in this domain to discuss this emerging world and its implications on data-management research. Among other topics, the discussion will touch on the opportunities for interesting research, how we can interact with other communities, what is the core expertise we bring to the table, and how we can conduct and evaluate this research effectively within our own community. The goal of the panel is to nudge the community to appreciate the opportunities in this new world of horizontal AI/ML and to spur a discussion on how we can shape an effective research agenda.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Association for Computing Machinery
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title
Opportunities for Data Management Research in the Era of Horizontal AI/ML
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
ethz.journal.title
Proceedings of the VLDB Endowment
ethz.journal.volume
12
en_US
ethz.journal.issue
12
en_US
ethz.journal.abbreviated
Proc. VLDB Endow.
ethz.pages.start
2323
en_US
ethz.pages.end
2324
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.event
45th International Conference on Very Large Data Bases (VLDB 2019)
en_US
ethz.event.location
Los Angeles, CA
en_US
ethz.event.date
August 26-30, 2019
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
New York, NY
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02663 - Institut für Computing Platforms / Institute for Computing Platforms::09588 - Zhang, Ce / Zhang, Ce
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02663 - Institut für Computing Platforms / Institute for Computing Platforms::09588 - Zhang, Ce / Zhang, Ce
ethz.date.deposited
2020-01-11T03:19:16Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-01-30T10:26:42Z
ethz.rosetta.lastUpdated
2020-02-15T23:55:52Z
ethz.rosetta.versionExported
true
ethz.COinS
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