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Dagstuhl-Seminar 23071

From Big Data Theory to Big Data Practice

( 12. Feb – 17. Feb, 2023 )

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Bitte benutzen Sie folgende Kurz-Url zum Verlinken dieser Seite: https://www.dagstuhl.de/23071

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Summary

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.

This Dagstuhl Seminar's aim was to address several questions by bringing together algorithmicists from these subcommunities, as well as algorithms engineers. Specifically, the aim was 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 have brought to light these deep connections. The goals were 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.

Organization of the Seminar

The seminar brought together 38 researchers from theoretical computer science and systems research, both from academia and from industry. The participants consisted of both senior and junior researchers, including a number of postdocs and advanced PhD students.

During the four (The seminar had to be cut short by one day due to unforeseen logistic issues.) days of the seminar, 20 talks of different lengths took place. Speakers were given no time constraints, but all the talks took between 30 to 60 minutes. We value the freedom given to the speakers, and the audience enjoyed the relaxed schedule as well. Each day afternoons were spent on fruitfull discussions and open-ended open problems sessions.

Outcome

The seminar was seen as a success by organizers and participants. It brought together relevant communities, shared state-of-the-art research, and discussed challenges. The talks were of high quality and stimulating, encouraging participants to actively engage in working groups during the afternoon and evenings. It was particularly appreciated that a significant number of younger researchers (postdocs and PhD students) participated and integrated well. The organizers would like to express their gratitude to the Scientific Directorate and the administration of the Dagstuhl Center for their strong support during the seminar.

Copyright Martin Farach-Colton, Fabian Daniel Kuhn, Ronitt Rubinfeld, and Przemyslaw Uznanski

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.
Copyright Martin Farach-Colton, Fabian Daniel Kuhn, Ronitt Rubinfeld, and Przemyslaw Uznanski

Teilnehmer

Klassifikation
  • Data Structures and Algorithms
  • Distributed / Parallel / and Cluster Computing

Schlagworte
  • Sublinear algorithms
  • local algorithms
  • external memory