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

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

doi:10.4230/DagMan.7.1.1

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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

Part of: Issue: Dagstuhl Manifestos, Volume 7, Issue 1
Part of: Volume: Dagstuhl Manifestos, Volume 7
Part of: Journal: Dagstuhl Manifestos (DagMan)
License: Creative Commons Attribution 3.0 Unported license
Publication Date: 2018-04-09

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Document Identifiers

DOI: 10.4230/DagMan.7.1.1
URN: urn:nbn:de:0030-drops-86772

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

Cite As Get BibTex

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

@Article{abiteboul_et_al:DagMan.7.1.1,
  author =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  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 =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.7.1.1},
  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, knowledge-enriched data management, machine learning, workflows, human-related data, ethics}
}

@Article{abiteboul_et_al:DagMan.7.1.1,
  author =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  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 =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagMan.7.1.1},
  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, knowledge-enriched data management, machine learning, workflows, human-related data, ethics}
}

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.

Subject Classification

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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