Dagstuhl-Seminar 25191
Adaptive and Scalable Data Structures
( 04. May – 09. May, 2025 )
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Organisatoren
- Michael A. Bender (Stony Brook University, US)
- John Iacono (ULB - Brussels, BE)
- László Kozma (FU Berlin, DE)
- Eva Rotenberg (Technical University of Denmark - Lyngby, DK)
Kontakt
- Marsha Kleinbauer (für wissenschaftliche Fragen)
- Jutka Gasiorowski (für administrative Fragen)
Dagstuhl Reports
As part of the mandatory documentation, participants are asked to submit their talk abstracts, working group results, etc. for publication in our series Dagstuhl Reports via the Dagstuhl Reports Submission System.
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Gemeinsame Dokumente
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Data structures are the science of organizing and accessing data, and their study is a core part of computer science. They underpin our computing infrastructure with efficiency being of critical importance. As the computing landscape changes with more demanding tasks arising, data structure research remains vibrant, with two aspects coming particularly in focus: scalability and adaptivity.
Scalability means that data structures remain efficient as data sets increase, become more dynamic, and become more distributed. Adaptivity implies taking advantage of modern hardware, such as multicore computation or memory hierarchies, as well specific structure and biases in the operations performed. One seeks to create structures that maximally take advantage of such architectural and distributional details without any foreknowledge of them. General limits of adaptivity have long posed deep theoretical questions, which continue to inspire research.
This Dagstuhl Seminar is part of a successful series begun in 1991. The series has contributed to shaping trends in data structures research. We propose to bring together leading researchers in classical data structures with those with expertise under the theme of scalability and adaptivity. By exposing the participants to diverse viewpoints, we aim to connect models and approaches, inspire new directions and collaborations, make progress on difficult problems, and continue advancing the state-of-the-art in data structures research.
Creative Commons BY 4.0
Michael A. Bender, John Iacono, László Kozma, and Eva Rotenberg
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- Peyman Afshani (Aarhus University, DK) [dblp]
- Kunal Agrawal (Washington University - St. Louis, US) [dblp]
- Hideo Bannai (Institute of Science Tokyo, JP) [dblp]
- Michael A. Bender (Stony Brook University, US) [dblp]
- Ioana Oriana Bercea (KTH Royal Institute of Technology - Stockholm, SE) [dblp]
- Philip Bille (Technical University of Denmark - Lyngby, DK) [dblp]
- Davide Bilò (University of L'Aquila, IT) [dblp]
- Gerth Stølting Brodal (Aarhus University, DK) [dblp]
- Shiri Chechik (Tel Aviv University, IL) [dblp]
- Alexander Conway (Cornell Tech - New York, US) [dblp]
- Justin Dallant (UL - Brussels, BE) [dblp]
- Aditi Dudeja (Paris Lodron Universität Salzburg, AT) [dblp]
- Faith Ellen (University of Toronto, CA) [dblp]
- Martin Farach-Colton (NYU - New York, US) [dblp]
- Jeremy Fineman (Georgetown University - Washington, DC, US) [dblp]
- Pawel Gawrychowski (University of Wroclaw, PL) [dblp]
- Michael Goodrich (University of California - Irvine, US) [dblp]
- Inge Li Gørtz (Technical University of Denmark - Lyngby, DK) [dblp]
- Joachim Gudmundsson (The University of Sydney, AU) [dblp]
- John Iacono (ULB - Brussels, BE) [dblp]
- Rob Johnson (Broadcom - San Jose, US) [dblp]
- Valerie King (University of Victoria, CA) [dblp]
- Tomasz Kociumaka (MPI für Informatik - Saarbrücken, DE) [dblp]
- Hanna Komlós (NYU - New York, US) [dblp]
- László Kozma (FU Berlin, DE) [dblp]
- William Kuszmaul (Carnegie Mellon University - Pittsburgh, US) [dblp]
- Jingxun Liang (Carnegie Mellon University - Pittsburgh, US)
- Quanquan C. Liu (Yale University - New Haven, US) [dblp]
- Ulrich Carsten Meyer (Goethe University - Frankfurt am Main, DE) [dblp]
- Ian Munro (University of Waterloo, CA) [dblp]
- Gonzalo Navarro (University of Chile - Santiago de Chile, CL) [dblp]
- Eva Rotenberg (Technical University of Denmark - Lyngby, DK) [dblp]
- Robert Sedgewick (Princeton University, US) [dblp]
- Marek Sokolowski (MPI für Informatik - Saarbrücken, DE) [dblp]
- Teresa Steiner (Technical University of Denmark - Lyngby, DK) [dblp]
- Sabine Storandt (Universität Konstanz, DE) [dblp]
- Robert Endre Tarjan (Princeton University, US) [dblp]
- Ivor van der Hoog (Technical University of Denmark - Lyngby, DK) [dblp]
- Stefan Walzer (KIT - Karlsruher Institut für Technologie, DE) [dblp]
- Nicole Wein (University of Michigan - Ann Arbor, US) [dblp]
- Huacheng Yu (Princeton University, US) [dblp]
- Or Zamir (Tel Aviv University, IL) [dblp]
- Renfei Zhou (Carnegie Mellon University - Pittsburgh, US) [dblp]
Verwandte Seminare
- Dagstuhl-Seminar 9145: Data Structures (1991-11-04 - 1991-11-08) (Details)
- Dagstuhl-Seminar 9409: Data Structures (1994-02-28 - 1994-03-04) (Details)
- Dagstuhl-Seminar 9609: Data Structures (1996-02-26 - 1996-03-01) (Details)
- Dagstuhl-Seminar 98091: Data Structures (1998-03-02 - 1998-03-06) (Details)
- Dagstuhl-Seminar 00091: Data Structures (2000-02-27 - 2000-03-03) (Details)
- Dagstuhl-Seminar 02091: Data Structures (2002-02-24 - 2002-03-01) (Details)
- Dagstuhl-Seminar 04091: Data Structures (2004-02-22 - 2004-02-27) (Details)
- Dagstuhl-Seminar 06091: Data Structures (2006-02-26 - 2006-03-03) (Details)
- Dagstuhl-Seminar 08081: Data Structures (2008-02-17 - 2008-02-22) (Details)
- Dagstuhl-Seminar 10091: Data Structures (2010-02-28 - 2010-03-05) (Details)
- Dagstuhl-Seminar 14091: Data Structures and Advanced Models of Computation on Big Data (2014-02-23 - 2014-02-28) (Details)
- Dagstuhl-Seminar 16101: Data Structures and Advanced Models of Computation on Big Data (2016-03-06 - 2016-03-11) (Details)
- Dagstuhl-Seminar 19051: Data Structures for the Cloud and External Memory Data (2019-01-27 - 2019-02-01) (Details)
- Dagstuhl-Seminar 21071: Scalable Data Structures (2021-02-14 - 2021-02-19) (Details)
- Dagstuhl-Seminar 23211: Scalable Data Structures (2023-05-21 - 2023-05-26) (Details)
Klassifikation
- Data Structures and Algorithms
Schlagworte
- Data structures
- Algorithms
- Big data
- Computational models
