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

Authors Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, Ke Yi



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Author Details

Serge Abiteboul
Marcelo Arenas
Pablo Barceló
Meghyn Bienvenu
Diego Calvanese
Claire David
Richard Hull
Eyke Hüllermeier
Benny Kimelfeld
Leonid Libkin
Wim Martens
Tova Milo
Filip Murlak
Frank Neven
Magdalena Ortiz
Thomas Schwentick
Julia Stoyanovich
Jianwen Su
Dan Suciu
Victor Vianu
Ke Yi

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Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi. Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). In Dagstuhl Manifestos, Volume 7, Issue 1, pp. 1-29, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/DagMan.7.1.1

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.
Keywords
  • database theory
  • principles of data management
  • query languages
  • efficient query processing
  • query optimization
  • heterogeneous data
  • uncertainty
  • knowledge-enriched data management
  • machine learning
  • workflows
  • human-related data
  • ethics

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