2 Search Results for "Cohen, Sara"

Document
DOI: 10.4230/LIPIcs.ITP.2021.10
Verified Progress Tracking for Timely Dataflow

Authors: Matthias Brun, Sára Decova, Andrea Lattuada, and Dmitriy Traytel

Published in: LIPIcs, Volume 193, 12th International Conference on Interactive Theorem Proving (ITP 2021)

Abstract
Large-scale stream processing systems often follow the dataflow paradigm, which enforces a program structure that exposes a high degree of parallelism. The Timely Dataflow distributed system supports expressive cyclic dataflows for which it offers low-latency data- and pipeline-parallel stream processing. To achieve high expressiveness and performance, Timely Dataflow uses an intricate distributed protocol for tracking the computation’s progress. We modeled the progress tracking protocol as a combination of two independent transition systems in the Isabelle/HOL proof assistant. We specified and verified the safety of the two components and of the combined protocol. To this end, we identified abstract assumptions on dataflow programs that are sufficient for safety and were not previously formalized.
Cite as

Matthias Brun, Sára Decova, Andrea Lattuada, and Dmitriy Traytel. Verified Progress Tracking for Timely Dataflow. In 12th International Conference on Interactive Theorem Proving (ITP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 193, pp. 10:1-10:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{brun_et_al:LIPIcs.ITP.2021.10,
  author =	{Brun, Matthias and Decova, S\'{a}ra and Lattuada, Andrea and Traytel, Dmitriy},
  title =	{{Verified Progress Tracking for Timely Dataflow}},
  booktitle =	{12th International Conference on Interactive Theorem Proving (ITP 2021)},
  pages =	{10:1--10:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-188-7},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{193},
  editor =	{Cohen, Liron and Kaliszyk, Cezary},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ITP.2021.10},
  URN =		{urn:nbn:de:0030-drops-139057},
  doi =		{10.4230/LIPIcs.ITP.2021.10},
  annote =	{Keywords: safety, distributed systems, timely dataflow, Isabelle/HOL}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2015.127
Learning Tree Patterns from Example Graphs

Authors: Sara Cohen and Yaacov Y. Weiss

Published in: LIPIcs, Volume 31, 18th International Conference on Database Theory (ICDT 2015)

Abstract
This paper investigates the problem of learning tree patterns that return nodes with a given set of labels, from example graphs provided by the user. Example graphs are annotated by the user as being either positive or negative. The goal is then to determine whether there exists a tree pattern returning tuples of nodes with the given labels in each of the positive examples, but in none of the negative examples, and, furthermore, to find one such pattern if it exists. These are called the satisfiability and learning problems, respectively. This paper thoroughly investigates the satisfiability and learning problems in a variety of settings. In particular, we consider example sets that (1) may contain only positive examples, or both positive and negative examples, (2) may contain directed or undirected graphs, and (3) may have multiple occurrences of labels or be uniquely labeled (to some degree). In addition, we consider tree patterns of different types that can allow, or prohibit, wildcard labeled nodes and descendant edges. We also consider two different semantics for mapping tree patterns to graphs. The complexity of satisfiability is determined for the different combinations of settings. For cases in which satisfiability is polynomial, it is also shown that learning is polynomial (This is non-trivial as satisfying patterns may be exponential in size). Finally, the minimal learning problem, i.e., that of finding a minimal-sized satisfying pattern, is studied for cases in which satisfiability is polynomial.
Cite as

Sara Cohen and Yaacov Y. Weiss. Learning Tree Patterns from Example Graphs. In 18th International Conference on Database Theory (ICDT 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 31, pp. 127-143, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)

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@InProceedings{cohen_et_al:LIPIcs.ICDT.2015.127,
  author =	{Cohen, Sara and Weiss, Yaacov Y.},
  title =	{{Learning Tree Patterns from Example Graphs}},
  booktitle =	{18th International Conference on Database Theory (ICDT 2015)},
  pages =	{127--143},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-79-8},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{31},
  editor =	{Arenas, Marcelo and Ugarte, Mart{\'\i}n},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2015.127},
  URN =		{urn:nbn:de:0030-drops-49819},
  doi =		{10.4230/LIPIcs.ICDT.2015.127},
  annote =	{Keywords: tree patterns, learning, examples}
}
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