ORCID: https://orcid.org/0000-0002-9944-4108; Kimelfeld, Benny; Libkin, Leonid; Martens, Wim; Milo, Tova; Murlak, Filip; Neven, Frank; Ortiz, Magdalena; Schwentick, Thomas; Stoyanovich, Julia; Su, Jianwen; Suciu, Dan; Vianu, Victor und Yi, Ke
(April 2018):
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151).
In: Dagstuhl Manifestos, Vol. 7, No. 1: pp. 1-29
[PDF, 519kB]

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.
Item Type: | Journal article |
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Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
URN: | urn:nbn:de:bvb:19-epub-107495-3 |
ISSN: | 2193-2433 |
Place of Publication: | Dagstuhl, Germany |
Item ID: | 107495 |
Date Deposited: | 23. Oct 2023 11:09 |
Last Modified: | 12. Oct 2024 20:09 |