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S. Abiteboul, M. Arenas, P. Barceló, M. Bienvenu, D. Calvanese, C. David, R. Hull, E. Hüllermeier, B. Kimelfeld, L. Libkin, W. Martens, T. Milo, F. Murlak, F. Neven, M. Ortiz de la Fuente, T. Schwentick, J. Stoyanovich, J. Su, D. Suciu, V. Vianu, K. Yi:
"Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)";
Dagstuhl Manifestos, 7 (2018), 1; 1 - 29.

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.

database theory, principles of data management, query languages, efficient query processing, query optimization, heterogeneous data, uncertainty, knowledge-enriched data management, machine learning, workflows, human-relat

http://dx.doi.org/10.4230/DagMan.7.1.1

http://drops.dagstuhl.de/opus/volltexte/2018/8677/pdf/dagman-v007-i001-p001-16151.pdf