DagRep.13.2.33.pdf
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From Big Data Theory to Big Data Practice (Dagstuhl Seminar 23071)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 13, Issue 2
Volume: Dagstuhl Reports, Volume 13
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2023-10-09
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Martin Farach-Colton, Fabian Daniel Kuhn, Ronitt Rubinfeld, and Przemysław Uznański. From Big Data Theory to Big Data Practice (Dagstuhl Seminar 23071). In Dagstuhl Reports, Volume 13, Issue 2, pp. 33-46, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/DagRep.13.2.33
https://doi.org/10.4230/DagRep.13.2.33
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23071 "From Big Data Theory to Big Data Practice". Some recent advances in the theory of algorithms for big data - sublinear/local algorithms, streaming algorithms and external memory algorithms - have translated into impressive improvements in practice, whereas others have remained stubbornly resistant to useful implementations. This seminar aimed to glean lessons for those aspect of these algorithms that have led to practical implementation to see if the lessons learned can both improve the implementations of other theoretical ideas and to help guide the next generation of theoretical advances.
Subject Classification
ACM Subject Classification
- Information systems → Data structures
- Theory of computation → Data structures design and analysis
- Theory of computation → Distributed algorithms
- Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
- external memory
- local algorithms
- sublinear algorithms
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