2 Search Results for "Davis, Jesse"

Document
DOI: 10.4230/DagRep.11.9.45
Machine Learning in Sports (Dagstuhl Seminar 21411)

Authors: Ulf Brefeld, Jesse Davis, Martin Lames, and James J. Little

Published in: Dagstuhl Reports, Volume 11, Issue 9 (2022)

Abstract
Data about sports have long been the subject of research and analysis by sports scientists. The increasing size and availability of these data have also attracted the attention of researchers in machine learning, computer vision and artificial intelligence. However, these communities rarely interact. This seminar aimed to bring together researchers from these areas to spur an interdisciplinary approach to these problems. The seminar was organized around five different themes that were introduced with tutorial and overview style talks about the key concepts to facilitate knowledge exchange among researchers with different backgrounds and approaches to data-based sports research. These were augmented by more in-depth presentations on specific problems or techniques. There was a panel discussion by practitioners on the difficulties and lessons learned about putting analytics into practice. Finally, we came up with a number of conclusions and next steps.
Cite as

Ulf Brefeld, Jesse Davis, Martin Lames, and James J. Little. Machine Learning in Sports (Dagstuhl Seminar 21411). In Dagstuhl Reports, Volume 11, Issue 9, pp. 45-63, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{brefeld_et_al:DagRep.11.9.45,
  author =	{Brefeld, Ulf and Davis, Jesse and Lames, Martin and Little, James J.},
  title =	{{Machine Learning in Sports (Dagstuhl Seminar 21411)}},
  pages =	{45--63},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{9},
  editor =	{Brefeld, Ulf and Davis, Jesse and Lames, Martin and Little, James J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.11.9.45},
  URN =		{urn:nbn:de:0030-drops-159178},
  doi =		{10.4230/DagRep.11.9.45},
  annote =	{Keywords: machine learning, artificial intelligence, sports science, computer vision, explanations, visualization, tactics, health, biomechanics}
}
Document
DOI: 10.4230/LIPIcs.OPODIS.2021.26
Design and Analysis of a Logless Dynamic Reconfiguration Protocol

Authors: William Schultz, Siyuan Zhou, Ian Dardik, and Stavros Tripakis

Published in: LIPIcs, Volume 217, 25th International Conference on Principles of Distributed Systems (OPODIS 2021)

Abstract
Distributed replication systems based on the replicated state machine model have become ubiquitous as the foundation of modern database systems. To ensure availability in the presence of faults, these systems must be able to dynamically replace failed nodes with healthy ones via dynamic reconfiguration. MongoDB is a document oriented database with a distributed replication mechanism derived from the Raft protocol. In this paper, we present MongoRaftReconfig, a novel dynamic reconfiguration protocol for the MongoDB replication system. MongoRaftReconfig utilizes a logless approach to managing configuration state and decouples the processing of configuration changes from the main database operation log. The protocol’s design was influenced by engineering constraints faced when attempting to redesign an unsafe, legacy reconfiguration mechanism that existed previously in MongoDB. We provide a safety proof of MongoRaftReconfig, along with a formal specification in TLA+. To our knowledge, this is the first published safety proof and formal specification of a reconfiguration protocol for a Raft-based system. We also present results from model checking the safety properties of MongoRaftReconfig on finite protocol instances. Finally, we discuss the conceptual novelties of MongoRaftReconfig, how it can be understood as an optimized and generalized version of the single server reconfiguration algorithm of Raft, and present an experimental evaluation of how its optimizations can provide performance benefits for reconfigurations.
Cite as

William Schultz, Siyuan Zhou, Ian Dardik, and Stavros Tripakis. Design and Analysis of a Logless Dynamic Reconfiguration Protocol. In 25th International Conference on Principles of Distributed Systems (OPODIS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 217, pp. 26:1-26:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{schultz_et_al:LIPIcs.OPODIS.2021.26,
  author =	{Schultz, William and Zhou, Siyuan and Dardik, Ian and Tripakis, Stavros},
  title =	{{Design and Analysis of a Logless Dynamic Reconfiguration Protocol}},
  booktitle =	{25th International Conference on Principles of Distributed Systems (OPODIS 2021)},
  pages =	{26:1--26:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-219-8},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{217},
  editor =	{Bramas, Quentin and Gramoli, Vincent and Milani, Alessia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2021.26},
  URN =		{urn:nbn:de:0030-drops-158016},
  doi =		{10.4230/LIPIcs.OPODIS.2021.26},
  annote =	{Keywords: Fault Tolerance, Dynamic Reconfiguration, State Machine Replication}
}
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