Research Directions for Principles of Data Management (Abridged)
Research Directions for Principles of Data Management (Abridged)
复制标题
数据管理原理研究方向(删节)
DOI:
10.1145/3092931.3092933
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发表时间:
2017
期刊:
影响因子:
--
通讯作者:
Abiteboul S
中科院分区:
文献类型:
--
作者:
Abiteboul S
In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organized jointly by the Executive Committee of the ACM Symposium on Principles of Database Systems (PODS) and the Council of the International Conference on Database Theory (ICDT). The mission of the workshop was to identify and explore some of the most important research directions that have high relevance to society and to Computer Science today, and where the PDM community has the potential to make significant contributions. This article presents a summary of the report created by the workshop [4]. That report describes the family of research directions that the workshop focused on from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term. The report organizes the identified research challenges for PDM around seven core themes, namely Managing Data at Scale, Multi-model Data, Uncertain Information, Knowledge-enriched Data, Data Management and Machine Learning, Process and Data, and Ethics and Data Management. Since new challenges in PDM arise all the time, we note that this list of themes is not intended to be exclusive.The Dagstuhl report is intended for a diverse audience, ranging from funding agencies, to universities and industrial research labs, to researchers and scientists who are exploring the many issues that arise in modern data management. The report is also intended for policy makers, sociologists, and philosophers, because it re-iterates the importance of considering ethics in many aspects of data creation, access, and usage, and suggests how research can help to find new ways for maximizing the benefits of massive data while nevertheless safeguarding the privacy and integrity of citizens and societies.
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DOI:
--
发表时间:
2011
期刊:
--
影响因子:
--
作者:
M. Akdere;U. Çetintemel;Matteo Riondato;E. Upfal;S. Zdonik
通讯作者:
M. Akdere;U. Çetintemel;Matteo Riondato;E. Upfal;S. Zdonik
DOI:
10.1145/2629565
发表时间:
2012-09
期刊:
ArXiv
影响因子:
--
作者:
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通讯作者:
A. Artale;R. Kontchakov;V. Ryzhikov;M. Zakharyaschev
DOI:
10.1145/2694428.2694441
发表时间:
2014-12
期刊:
SIGMOD Rec.
影响因子:
--
作者:
D. Abadi;R. Agrawal;A. Ailamaki;M. Balazinska;P. Bernstein;M. Carey;S. Chaudhuri;Jeffrey Dean;A. Doan;M. Franklin;J. Gehrke;L. Haas;A. Halevy;J. Hellerstein;Y. Ioannidis;H. V. Jagadish;Donald Kossmann;S. Madden;S. Mehrotra;Tova Milo;J. Naughton;R. Ramakrishnan;V. Markl;Christopher Olston;B. Ooi;C. Ré;Dan Suciu;M. Stonebraker;Todd Walter;J. Widom
通讯作者:
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DOI:
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发表时间:
1994
期刊:
Journal of computer and system sciences (Print)
影响因子:
--
作者:
Dan Suciu;V. Tannen
通讯作者:
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DOI:
--
发表时间:
2011
期刊:
International Conference on Database Theory
影响因子:
--
作者:
Dan Suciu
通讯作者:
Dan Suciu