https://www.dagstuhl.de/23071

12. – 17. Februar 2023, Dagstuhl-Seminar 23071

From Big Data Theory to Big Data Practice

Organisatoren

Martin Farach-Colton (Rutgers University – Piscataway, US)
Fabian Daniel Kuhn (Universität Freiburg, DE)
Ronitt Rubinfeld (MIT – Cambridge, US)
Przemyslaw Uznanski (University of Wroclaw, PL)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Susanne Bach-Bernhard zu administrativen Fragen

Andreas Dolzmann zu wissenschaftlichen Fragen

Motivation

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 Dagstuhl Seminar aims to glean lessons for those aspects 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.

As data has grown faster than RAM, the theory of algorithms has expanded to provide approaches for tackling such problems. These fall into three broad categories:

  • Streaming and semi-streaming algorithms
  • Sublinear or local algorithms
  • External memory algorithms

Each of these areas has a vibrant literature, and many of the results from the theory literature have made their way into practice. Other results are not suitable for implementation and deployment. The seminar aims to address several questions by bringing together algorithmicists from these subcommunities, as well as algorithms engineers. Specifically, we aim to address the following questions:

  • What themes emerge from considering practical algorithms from the theory literature?
  • Can we use these insights to create new models or to capture interesting new optimization criteria?

By bringing together researchers in these disparate areas and by including researchers in algorithms engineering, we hope to bring to light these deep connections. The goals are to:

  • Extract shared lessons to help guide theoretical research towards practical solutions;
  • Create a feedback loop where commonalities of practical solutions can help guide future theoretical research;
  • Help cross-pollinate these research areas.

Motivation text license
  Creative Commons BY 4.0
  Martin Farach-Colton, Fabian Daniel Kuhn, Ronitt Rubinfeld, and Przemyslaw Uznanski

Classification

  • Data Structures And Algorithms
  • Distributed / Parallel / And Cluster Computing

Keywords

  • Sublinear algorithms
  • Local algorithms
  • External memory

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Dagstuhl's Impact

Bitte informieren Sie uns, wenn eine Veröffentlichung ausgehend von Ihrem Seminar entsteht. Derartige Veröffentlichungen werden von uns in der Rubrik Dagstuhl's Impact separat aufgelistet  und im Erdgeschoss der Bibliothek präsentiert.

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.