License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
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DOI: 10.4230/DagMan.7.1.1
URN: urn:nbn:de:0030-drops-86772
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/8677/
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Abiteboul, Serge ; Arenas, Marcelo ; Barceló, Pablo ; Bienvenu, Meghyn ; Calvanese, Diego ; David, Claire ; Hull, Richard ; Hüllermeier, Eyke ; Kimelfeld, Benny ; Libkin, Leonid ; Martens, Wim ; Milo, Tova ; Murlak, Filip ; Neven, Frank ; Ortiz, Magdalena ; Schwentick, Thomas ; Stoyanovich, Julia ; Su, Jianwen ; Suciu, Dan ; Vianu, Victor ; Yi, Ke
Weitere Beteiligte (Hrsg. etc.): Serge Abiteboul et al.

Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)

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dagman-v007-i001-p001-16151.pdf (0.5 MB)

Abstract

The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing
data and knowledge. This work has involved a rich cross-fertilization between
PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate on-going expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with.

In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.

BibTeX - Entry

@Article{abiteboul_et_al:DM:2018:8677,
  author =	{Serge Abiteboul and Marcelo Arenas and Pablo Barcel{\'o} and Meghyn Bienvenu and Diego Calvanese and Claire David and Richard Hull and Eyke H{\"u}llermeier and Benny Kimelfeld and Leonid Libkin and Wim Martens and Tova Milo and Filip Murlak and Frank Neven and Magdalena Ortiz and Thomas Schwentick and Julia Stoyanovich and Jianwen Su and Dan Suciu and Victor Vianu and Ke Yi},
  title =	{{Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)}},
  pages =	{1--29},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2018},
  volume =	{7},
  number =	{1},
  editor =	{Serge Abiteboul et al.},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/8677},
  URN =		{urn:nbn:de:0030-drops-86772},
  doi =		{10.4230/DagMan.7.1.1},
  annote =	{Keywords: database theory, principles of data management, query languages,  efficient query processing, query optimization, heterogeneous data, uncertainty, }
}

Keywords: database theory, principles of data management, query languages, efficient query processing, query optimization, heterogeneous data, uncertainty,
Freie Schlagwörter (englisch): knowledge-enriched data management, machine learning, workflows, human-related data, ethics
Collection: Dagstuhl Manifestos, Volume 7, Issue 1
Issue Date: 2018
Date of publication: 09.04.2018

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