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Documents authored by Groz, Benoit

Found 2 Possible Name Variants:

Groz, Benoit

Document
DOI: 10.4230/LIPIcs.ITCS.2025.5
Edge-Minimum Walk of Modular Length in Polynomial Time

Authors: Antoine Amarilli, Benoît Groz, and Nicole Wein

Published in: LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)

Abstract
We study the problem of finding, in a directed graph, an st-walk of length r od q which is edge-minimum, i.e., uses the smallest number of distinct edges. Despite the vast literature on paths and cycles with modularity constraints, to the best of our knowledge we are the first to study this problem. Our main result is a polynomial-time algorithm that solves this task when r and q are constants. We also show how our proof technique gives an algorithm to solve a generalization of the well-known Directed Steiner Network problem, in which connections between endpoint pairs are required to satisfy modularity constraints on their length. Our algorithm is polynomial when the number of endpoint pairs and the modularity constraints on the pairs are constants. In this version of the article, proofs and examples are omitted because of space constraints. Detailed proofs are available in the full version [Antoine Amarilli et al., 2024].
Cite as

Antoine Amarilli, Benoît Groz, and Nicole Wein. Edge-Minimum Walk of Modular Length in Polynomial Time. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 5:1-5:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{amarilli_et_al:LIPIcs.ITCS.2025.5,
  author =	{Amarilli, Antoine and Groz, Beno\^{i}t and Wein, Nicole},
  title =	{{Edge-Minimum Walk of Modular Length in Polynomial Time}},
  booktitle =	{16th Innovations in Theoretical Computer Science Conference (ITCS 2025)},
  pages =	{5:1--5:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-361-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{325},
  editor =	{Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.5},
  URN =		{urn:nbn:de:0030-drops-226330},
  doi =		{10.4230/LIPIcs.ITCS.2025.5},
  annote =	{Keywords: Directed Steiner Network, Modularity}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2022.14
Inference of Shape Graphs for Graph Databases

Authors: Benoît Groz, Aurélien Lemay, Sławek Staworko, and Piotr Wieczorek

Published in: LIPIcs, Volume 220, 25th International Conference on Database Theory (ICDT 2022)

Abstract
We investigate the problem of constructing a shape graph that describes the structure of a given graph database. We employ the framework of grammatical inference, where the objective is to find an inference algorithm that is both sound, i.e., always producing a schema that validates the input graph, and complete, i.e., able to produce any schema, within a given class of schemas, provided that a sufficiently informative input graph is presented. We identify a number of fundamental limitations that preclude feasible inference. We present inference algorithms based on natural approaches that allow to infer schemas that we argue to be of practical importance.
Cite as

Benoît Groz, Aurélien Lemay, Sławek Staworko, and Piotr Wieczorek. Inference of Shape Graphs for Graph Databases. In 25th International Conference on Database Theory (ICDT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 220, pp. 14:1-14:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{groz_et_al:LIPIcs.ICDT.2022.14,
  author =	{Groz, Beno\^{i}t and Lemay, Aur\'{e}lien and Staworko, S{\l}awek and Wieczorek, Piotr},
  title =	{{Inference of Shape Graphs for Graph Databases}},
  booktitle =	{25th International Conference on Database Theory (ICDT 2022)},
  pages =	{14:1--14:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-223-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{220},
  editor =	{Olteanu, Dan and Vortmeier, Nils},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2022.14},
  URN =		{urn:nbn:de:0030-drops-158889},
  doi =		{10.4230/LIPIcs.ICDT.2022.14},
  annote =	{Keywords: RDF, Schema, Inference, Learning, Fitting, Minimality, Containment}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2016.12
Filtering With the Crowd: CrowdScreen Revisited

Authors: Benoit Groz, Ezra Levin, Isaac Meilijson, and Tova Milo

Published in: LIPIcs, Volume 48, 19th International Conference on Database Theory (ICDT 2016)

Abstract
Filtering a set of items, based on a set of properties that can be verified by humans, is a common application of CrowdSourcing. When the workers are error-prone, each item is presented to multiple users, to limit the probability of misclassification. Since the Crowd is a relatively expensive resource, minimizing the number of questions per item may naturally result in big savings. Several algorithms to address this minimization problem have been presented in the CrowdScreen framework by Parameswaran et al. However, those algorithms do not scale well and therefore cannot be used in scenarios where high accuracy is required in spite of high user error rates. The goal of this paper is thus to devise algorithms that can cope with such situations. To achieve this, we provide new theoretical insights to the problem, then use them to develop a new efficient algorithm. We also propose novel optimizations for the algorithms of CrowdScreen that improve their scalability. We complement our theoretical study by an experimental evaluation of the algorithms on a large set of synthetic parameters as well as real-life crowdsourcing scenarios, demonstrating the advantages of our solution.
Cite as

Benoit Groz, Ezra Levin, Isaac Meilijson, and Tova Milo. Filtering With the Crowd: CrowdScreen Revisited. In 19th International Conference on Database Theory (ICDT 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 48, pp. 12:1-12:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{groz_et_al:LIPIcs.ICDT.2016.12,
  author =	{Groz, Benoit and Levin, Ezra and Meilijson, Isaac and Milo, Tova},
  title =	{{Filtering With the Crowd: CrowdScreen Revisited}},
  booktitle =	{19th International Conference on Database Theory (ICDT 2016)},
  pages =	{12:1--12:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-002-6},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{48},
  editor =	{Martens, Wim and Zeume, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2016.12},
  URN =		{urn:nbn:de:0030-drops-57817},
  doi =		{10.4230/LIPIcs.ICDT.2016.12},
  annote =	{Keywords: CrowdSourcing, filtering, algorithms, sprt, hypothesis testing}
}

Groz, Benoît

Document
DOI: 10.4230/LIPIcs.ITCS.2025.5
Edge-Minimum Walk of Modular Length in Polynomial Time

Authors: Antoine Amarilli, Benoît Groz, and Nicole Wein

Published in: LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)

Abstract
We study the problem of finding, in a directed graph, an st-walk of length r od q which is edge-minimum, i.e., uses the smallest number of distinct edges. Despite the vast literature on paths and cycles with modularity constraints, to the best of our knowledge we are the first to study this problem. Our main result is a polynomial-time algorithm that solves this task when r and q are constants. We also show how our proof technique gives an algorithm to solve a generalization of the well-known Directed Steiner Network problem, in which connections between endpoint pairs are required to satisfy modularity constraints on their length. Our algorithm is polynomial when the number of endpoint pairs and the modularity constraints on the pairs are constants. In this version of the article, proofs and examples are omitted because of space constraints. Detailed proofs are available in the full version [Antoine Amarilli et al., 2024].
Cite as

Antoine Amarilli, Benoît Groz, and Nicole Wein. Edge-Minimum Walk of Modular Length in Polynomial Time. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 5:1-5:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{amarilli_et_al:LIPIcs.ITCS.2025.5,
  author =	{Amarilli, Antoine and Groz, Beno\^{i}t and Wein, Nicole},
  title =	{{Edge-Minimum Walk of Modular Length in Polynomial Time}},
  booktitle =	{16th Innovations in Theoretical Computer Science Conference (ITCS 2025)},
  pages =	{5:1--5:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-361-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{325},
  editor =	{Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.5},
  URN =		{urn:nbn:de:0030-drops-226330},
  doi =		{10.4230/LIPIcs.ITCS.2025.5},
  annote =	{Keywords: Directed Steiner Network, Modularity}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2022.14
Inference of Shape Graphs for Graph Databases

Authors: Benoît Groz, Aurélien Lemay, Sławek Staworko, and Piotr Wieczorek

Published in: LIPIcs, Volume 220, 25th International Conference on Database Theory (ICDT 2022)

Abstract
We investigate the problem of constructing a shape graph that describes the structure of a given graph database. We employ the framework of grammatical inference, where the objective is to find an inference algorithm that is both sound, i.e., always producing a schema that validates the input graph, and complete, i.e., able to produce any schema, within a given class of schemas, provided that a sufficiently informative input graph is presented. We identify a number of fundamental limitations that preclude feasible inference. We present inference algorithms based on natural approaches that allow to infer schemas that we argue to be of practical importance.
Cite as

Benoît Groz, Aurélien Lemay, Sławek Staworko, and Piotr Wieczorek. Inference of Shape Graphs for Graph Databases. In 25th International Conference on Database Theory (ICDT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 220, pp. 14:1-14:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{groz_et_al:LIPIcs.ICDT.2022.14,
  author =	{Groz, Beno\^{i}t and Lemay, Aur\'{e}lien and Staworko, S{\l}awek and Wieczorek, Piotr},
  title =	{{Inference of Shape Graphs for Graph Databases}},
  booktitle =	{25th International Conference on Database Theory (ICDT 2022)},
  pages =	{14:1--14:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-223-5},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{220},
  editor =	{Olteanu, Dan and Vortmeier, Nils},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2022.14},
  URN =		{urn:nbn:de:0030-drops-158889},
  doi =		{10.4230/LIPIcs.ICDT.2022.14},
  annote =	{Keywords: RDF, Schema, Inference, Learning, Fitting, Minimality, Containment}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2016.12
Filtering With the Crowd: CrowdScreen Revisited

Authors: Benoit Groz, Ezra Levin, Isaac Meilijson, and Tova Milo

Published in: LIPIcs, Volume 48, 19th International Conference on Database Theory (ICDT 2016)

Abstract
Filtering a set of items, based on a set of properties that can be verified by humans, is a common application of CrowdSourcing. When the workers are error-prone, each item is presented to multiple users, to limit the probability of misclassification. Since the Crowd is a relatively expensive resource, minimizing the number of questions per item may naturally result in big savings. Several algorithms to address this minimization problem have been presented in the CrowdScreen framework by Parameswaran et al. However, those algorithms do not scale well and therefore cannot be used in scenarios where high accuracy is required in spite of high user error rates. The goal of this paper is thus to devise algorithms that can cope with such situations. To achieve this, we provide new theoretical insights to the problem, then use them to develop a new efficient algorithm. We also propose novel optimizations for the algorithms of CrowdScreen that improve their scalability. We complement our theoretical study by an experimental evaluation of the algorithms on a large set of synthetic parameters as well as real-life crowdsourcing scenarios, demonstrating the advantages of our solution.
Cite as

Benoit Groz, Ezra Levin, Isaac Meilijson, and Tova Milo. Filtering With the Crowd: CrowdScreen Revisited. In 19th International Conference on Database Theory (ICDT 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 48, pp. 12:1-12:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

Copy BibTex To Clipboard

@InProceedings{groz_et_al:LIPIcs.ICDT.2016.12,
  author =	{Groz, Benoit and Levin, Ezra and Meilijson, Isaac and Milo, Tova},
  title =	{{Filtering With the Crowd: CrowdScreen Revisited}},
  booktitle =	{19th International Conference on Database Theory (ICDT 2016)},
  pages =	{12:1--12:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-002-6},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{48},
  editor =	{Martens, Wim and Zeume, Thomas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2016.12},
  URN =		{urn:nbn:de:0030-drops-57817},
  doi =		{10.4230/LIPIcs.ICDT.2016.12},
  annote =	{Keywords: CrowdSourcing, filtering, algorithms, sprt, hypothesis testing}
}
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