3 Search Results for "Hong, Sungpack"

Document
DOI: 10.4230/LIPIcs.GD.2025.38
Stress in Graph Drawings: Perception, Preference, and Performance

Authors: Gavin J. Mooney, Jacob Miller, Michael Wybrow, Stephen Kobourov, and Helen C. Purchase

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
Stress in a graph drawing has been a popular layout principle for more than two decades. Low stress drawings exhibit the property that the geometric distances between all pairs of nodes correlate with the shortest paths between them. The assumption has always been that low stress drawings are "nicer" and better support human perception and comprehension than high stress drawings. In this paper, we put these assumptions to the test. We use a normalised scale-independent and rotation-independent metric for stress; this is necessary to ensure strict controls on our experimental stimuli. We report on three experiments, exploring human perception of stress, preference for stress, and the effect of stress on a graph performance task. We conclude that people can see stress in a graph drawing, that they prefer low stress drawings, and that their performance in a shortest path task improves as stress decreases - thus empirically confirming long-standing assumptions.
Cite as

Gavin J. Mooney, Jacob Miller, Michael Wybrow, Stephen Kobourov, and Helen C. Purchase. Stress in Graph Drawings: Perception, Preference, and Performance. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 38:1-38:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mooney_et_al:LIPIcs.GD.2025.38,
  author =	{Mooney, Gavin J. and Miller, Jacob and Wybrow, Michael and Kobourov, Stephen and Purchase, Helen C.},
  title =	{{Stress in Graph Drawings: Perception, Preference, and Performance}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{38:1--38:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.38},
  URN =		{urn:nbn:de:0030-drops-250240},
  doi =		{10.4230/LIPIcs.GD.2025.38},
  annote =	{Keywords: Graph Drawing, Graph Drawing Metrics, Stress, Visual Perception, User Study}
}
Document
DOI: 10.4230/OASIcs.EVCS.2023.5
Spoofax at Oracle: Domain-Specific Language Engineering for Large-Scale Graph Analytics

Authors: Houda Boukham, Guido Wachsmuth, Toine Hartman, Hamza Boucherit, Oskar van Rest, Hassan Chafi, Sungpack Hong, Martijn Dwars, Arnaud Delamare, and Dalila Chiadmi

Published in: OASIcs, Volume 109, Eelco Visser Commemorative Symposium (EVCS 2023)

Abstract
For the last decade, teams at Oracle relied on the Spoofax language workbench to develop a family of domain-specific languages for graph analytics in research projects and in product development. In this paper, we analyze the requirements for integrating language processors into large-scale graph analytics toolkits and for the development of these language processors as part of a larger product development process. We discuss how Spoofax helps to meet these requirements and point out the need for future improvements.
Cite as

Houda Boukham, Guido Wachsmuth, Toine Hartman, Hamza Boucherit, Oskar van Rest, Hassan Chafi, Sungpack Hong, Martijn Dwars, Arnaud Delamare, and Dalila Chiadmi. Spoofax at Oracle: Domain-Specific Language Engineering for Large-Scale Graph Analytics. In Eelco Visser Commemorative Symposium (EVCS 2023). Open Access Series in Informatics (OASIcs), Volume 109, pp. 5:1-5:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{boukham_et_al:OASIcs.EVCS.2023.5,
  author =	{Boukham, Houda and Wachsmuth, Guido and Hartman, Toine and Boucherit, Hamza and van Rest, Oskar and Chafi, Hassan and Hong, Sungpack and Dwars, Martijn and Delamare, Arnaud and Chiadmi, Dalila},
  title =	{{Spoofax at Oracle: Domain-Specific Language Engineering for Large-Scale Graph Analytics}},
  booktitle =	{Eelco Visser Commemorative Symposium (EVCS 2023)},
  pages =	{5:1--5:8},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-267-9},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{109},
  editor =	{L\"{a}mmel, Ralf and Mosses, Peter D. and Steimann, Friedrich},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.EVCS.2023.5},
  URN =		{urn:nbn:de:0030-drops-177756},
  doi =		{10.4230/OASIcs.EVCS.2023.5},
  annote =	{Keywords: language workbench, domain-specific language}
}
Document
DOI: 10.4230/LIPIcs.OPODIS.2020.17
CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure

Authors: Soukaina Firmli, Vasileios Trigonakis, Jean-Pierre Lozi, Iraklis Psaroudakis, Alexander Weld, Dalila Chiadmi, Sungpack Hong, and Hassan Chafi

Published in: LIPIcs, Volume 184, 24th International Conference on Principles of Distributed Systems (OPODIS 2020)

Abstract
The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph-processing system lies a concurrent graph data structure storing the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, graph-processing systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and low-memory graph mutations. Existing graph structures offer a hard trade-off between read-only performance, update friendliness, and memory consumption upon updates. In this paper, we introduce CSR++, a new graph data structure that removes these trade-offs and enables both fast read-only analytics and quick and memory-friendly mutations. CSR++ combines ideas from CSR, the fastest read-only data structure, and adjacency lists to achieve the best of both worlds. We compare CSR++ to CSR, adjacency lists from the Boost Graph Library, and LLAMA, a state-of-the-art update-friendly graph structure. In our evaluation, which is based on popular graph-processing algorithms executed over real-world graphs, we show that CSR++ remains close to CSR in read-only concurrent performance (within 10% on average), while significantly outperforming CSR (by an order of magnitude) and LLAMA (by almost 2×) with frequent updates.
Cite as

Soukaina Firmli, Vasileios Trigonakis, Jean-Pierre Lozi, Iraklis Psaroudakis, Alexander Weld, Dalila Chiadmi, Sungpack Hong, and Hassan Chafi. CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure. In 24th International Conference on Principles of Distributed Systems (OPODIS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 184, pp. 17:1-17:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{firmli_et_al:LIPIcs.OPODIS.2020.17,
  author =	{Firmli, Soukaina and Trigonakis, Vasileios and Lozi, Jean-Pierre and Psaroudakis, Iraklis and Weld, Alexander and Chiadmi, Dalila and Hong, Sungpack and Chafi, Hassan},
  title =	{{CSR++: A Fast, Scalable, Update-Friendly Graph Data Structure}},
  booktitle =	{24th International Conference on Principles of Distributed Systems (OPODIS 2020)},
  pages =	{17:1--17:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-176-4},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{184},
  editor =	{Bramas, Quentin and Oshman, Rotem and Romano, Paolo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2020.17},
  URN =		{urn:nbn:de:0030-drops-135021},
  doi =		{10.4230/LIPIcs.OPODIS.2020.17},
  annote =	{Keywords: Data Structures, Concurrency, Graph Processing, Graph Mutations}
}
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