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

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

Technical Report, e-Print archive. CoRR Technical Report arXiv:1701.09007 2017. Available at

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.

   title = "Research Directions for Principles of Data Management (Dagstuhl
Perspectives Workshop 16151)",
   year = "2017",
   author = "Serge Abiteboul and Marcelo Arenas and Pablo Barceló
and Meghyn Bienvenu and Diego Calvanese and Claire David and Richard
Hull and Eyke Hüllermeier 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",
   institution = " e-Print archive",
   number = "arXiv:1701.09007",
   note = "Available at",
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