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236 results on '"Auber, David"'

6. Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach

Author

Wallinger, Markus, Archambault, Daniel, Auber, David, Nöllenburg, Martin, and Peltonen, Jaakko

Subjects
Computer Science - Graphics
Abstract

Edge bundling techniques cluster edges with similar attributes (i.e. similarity in direction and proximity) together to reduce the visual clutter. All edge bundling techniques to date implicitly or explicitly cluster groups of individual edges, or parts of them, together based on these attributes. These clusters can result in ambiguous connections that do not exist in the data. Confluent drawings of networks do not have these ambiguities, but require the layout to be computed as part of the bundling process. We devise a new bundling method, Edge-Path bundling, to simplify edge clutter while greatly reducing ambiguities compared to previous bundling techniques. Edge-Path bundling takes a layout as input and clusters each edge along a weighted, shortest path to limit its deviation from a straight line. Edge-Path bundling does not incur independent edge ambiguities typically seen in all edge bundling methods, and the level of bundling can be tuned through shortest path distances, Euclidean distances, and combinations of the two. Also, directed edge bundling naturally emerges from the model. Through metric evaluations, we demonstrate the advantages of Edge-Path bundling over other techniques., Comment: VIS 2021

Published
2021
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7. Deep Neural Network for DrawiNg Networks, (DNN)^2

Author

Giovannangeli, Loann, Lalanne, Frederic, Auber, David, Giot, Romain, and Bourqui, Romain

Subjects
Computer Science - Machine Learning
Abstract

By leveraging recent progress of stochastic gradient descent methods, several works have shown that graphs could be efficiently laid out through the optimization of a tailored objective function. In the meantime, Deep Learning (DL) techniques achieved great performances in many applications. We demonstrate that it is possible to use DL techniques to learn a graph-to-layout sequence of operations thanks to a graph-related objective function. In this paper, we present a novel graph drawing framework called (DNN)^2: Deep Neural Network for DrawiNg Networks. Our method uses Graph Convolution Networks to learn a model. Learning is achieved by optimizing a graph topology related loss function that evaluates (DNN)^2 generated layouts during training. Once trained, the (DNN)^ model is able to quickly lay any input graph out. We experiment (DNN)^2 and statistically compare it to optimization-based and regular graph layout algorithms. The results show that (DNN)^2 performs well and are encouraging as the Deep Learning approach to Graph Drawing is novel and many leads for future works are identified., Comment: Appears in the Proceedings of the 29th International Symposium on Graph Drawing and Network Visualization (GD 2021)

Published
2021

8. Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation

Author

Giovannangeli, Loann, Giot, Romain, Auber, David, and Bourqui, Romain

Subjects
Computer Science - Graphics, Computer Science - Artificial Intelligence
Abstract

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data. In these representations, data are encoded with various visual attributes depending on the needs of the representation itself. To make coherent design choices about visual attributes, the visual search field proposes guidelines based on the human brain perception of features. However, information visualization representations frequently need to depict more data than the amount these guidelines have been validated on. Since, the information visualization community has extended these guidelines to a wider parameter space. This paper contributes to this theme by extending visual search theories to an information visualization context. We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractor. Stimuli are defined by color and shape features for the purpose of visually encoding categorical data. The experimental protocol is made of a parameters space reduction step (i.e., sub-sampling) based on a machine learning model, and a user evaluation to measure capacity limits and validate hypotheses. The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier. When redundantly encoded, the display heterogeneity has no effect on the task. When encoded with one attribute, the difficulty depends on that attribute heterogeneity until its capacity limit (7 for color, 5 for shape) is reached. Finally, when encoded with two attributes simultaneously, performances drop drastically even with minor heterogeneity.

Published
2021

9. Graph Drawing and Network Visualization GD2020

Author

Auber, David and Valtr, Pavel

Subjects
Computer Science - Computational Geometry, Computer Science - Discrete Mathematics
Abstract

Proceedings of GD2020: This volume contains the papers presented at GD~2020, the 28th International Symposium on Graph Drawing and Network Visualization, held on September 18-20, 2020 online. Graph drawing is concerned with the geometric representation of graphs and constitutes the algorithmic core of network visualization. Graph drawing and network visualization are motivated by applications where it is crucial to visually analyse and interact with relational datasets. Information about the conference series and past symposia is maintained at http://www.graphdrawing.org. The 2020 edition of the conference was hosted by University Of British Columbia, with Will Evans as chair of the Organizing Committee. A total of 251 participants attended the conference., Comment: Proceedings of GD2020

Published
2021

11. List of contributors

Author

Abdiyeva, Kamila, primary, Ahuja, Narendra, additional, Anneken, Mathias, additional, Auber, David, additional, Ayyar, Meghna P., additional, Beddiar, Romaissa, additional, Benois-Pineau, Jenny, additional, Bescós, Jesús, additional, Boscolo Galazzo, Ilaria, additional, Bourqui, Romain, additional, Brusini, Lorenza, additional, Burkart, Nadia, additional, Calabrese, Massimiliano, additional, Cruciani, Federica, additional, Delaney, Eoin, additional, Deriche, Rachid, additional, Escudero-Viñolo, Marcos, additional, Gajić, Andrija, additional, Garreau, Damien, additional, Giacinto, Giorgio, additional, Giot, Romain, additional, Gorokhovatskyi, Oleksii, additional, Gorokhovatskyi, Volodymyr, additional, Greene, Derek, additional, Halnaut, Adrien, additional, Hardouin, Alexandre, additional, Huber, Marco F., additional, Jouis, Gaëlle, additional, Keane, Mark T., additional, Kenny, Eoin M., additional, López-Cifuentes, Alejandro, additional, Lukac, Martin, additional, Menegaz, Gloria, additional, Mouchère, Harold, additional, Oussalah, Mourad, additional, Peredrii, Olena, additional, Petkovic, Dragutin, additional, Picarougne, Fabien, additional, Quénot, Georges, additional, Retuci Pinheiro, Gustavo, additional, Rieck, Konrad, additional, Rittner, Leticia, additional, Samek, Wojciech, additional, Scalas, Michele, additional, Setti, Francesco, additional, Veerappa, Manjunatha, additional, Vlasenko, Nataliia, additional, Zemmari, Akka, additional, and Zucchelli, Mauro, additional

Published
2023
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13. Data-Oriented Algorithm for Real-Time Estimation of Flow Rates and Flow Directions in a Water Distribution Network

Author

Dumora, Christophe, Auber, David, Bigot, Jérémie, Couallier, Vincent, and Leclerc, Cyril

Subjects
Physics - Physics and Society, Computer Science - Data Structures and Algorithms, Computer Science - Information Retrieval
Abstract

The aim of this paper is to present how data collected from a water distribution network (WDN) can be used to reconstruct flow rate and flow direction all over the network to enhance knowledge and detection of unforeseen events. The methodological approach consists in modeling the WDN and all available sensor data related to the management of such a network in the form of a flow network graph G = (V, E, s, t, c), with V a set of nodes, E a set of edges whose elements are ordered pairs of distinct nodes, s a source node, t a sink node and c a capacity function on edges. Our objective is to reconstruct a real-valued function f(u,v): VxV => R on all the edges E in VxV from partial observations on a small number of nodes V = {1, ..., n}. This reconstruction method consists in a data-driven Ford-Fulkerson maximum-flow problem in a multi-source, multi-sink context using a constrained bidirectional breadth-first search based on Edmonds-Karp method. The innovative approach is its application in the context of smart cities to operate from sensor data, structural data from a geographical information system (GIS) and consumption estimates.

Published
2018

15. Deep Neural Network for DrawiNg Networks,

Author

Giovannangeli, Loann, Lalanne, Frederic, Auber, David, Giot, Romain, Bourqui, Romain, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Purchase, Helen C., editor, and Rutter, Ignaz, editor

Published
2021
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16. Samples Classification Analysis Across DNN Layers with Fractal Curves

Author

Halnaut, Adrien, Giot, Romain, Bourqui, Romain, Auber, David, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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19. Tulip 5

Author

Auber, David, Archambault, Daniel, Bourqui, Romain, Delest, Maylis, Dubois, Jonathan, Lambert, Antoine, Mary, Patrick, Mathiaut, Morgan, Melançon, Guy, Pinaud, Bruno, Renoust, Benjamin, Vallet, Jason, Batagelj, Vladimir, Section Editor, Alhajj, Reda, editor, and Rokne, Jon, editor

Published
2018
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22. Rook-Drawing for Plane Graphs

Author

Auber, David, Bonichon, Nicolas, Dorbec, Paul, Pennarun, Claire, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Di Giacomo, Emilio, editor, and Lubiw, Anna, editor

Published
2015
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23. Tulip III

Author

Auber, David, Archambault, Daniel, Bourqui, Romain, Delest, Maylis, Dubois, Jonathan, Pinaud, Bruno, Lambert, Antoine, Mary, Patrick, Mathiaut, Morgan, Melancon, Guy, Alhajj, Reda, editor, and Rokne, Jon, editor

Published
2014
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24. Toward Efficient Deep Learning for Graph Drawing (DL4GD)

Author

Giovannangeli, Loann, Lalanne, Frederic, Auber, David, Giot, Romain, and Bourqui, Romain

Abstract

Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various complicated tasks such as graph classification and edge prediction. Eventually, they reached the Graph Drawing (GD) task. This article is an extended version of the previously published (DNN)2 and presents a framework to leverage DL techniques for graph drawing (DL4GD). We demonstrate how it is possible to train a Deep Learning model to extract features from a graph and project them into a graph layout. The method proposes to leverage efficient Convolutional Neural Networks, adapting them to graphs using Graph Convolutions. The graph layout projection is learned by optimizing a cost function that does not require any ground truth layout, as opposed to prior work. This paper also proposes an implementation and benchmark of the framework to study its sensitivity to certain deep learning-related conditions. As the field is novel, and many questions remain to be answered, we do not focus on finding the most optimal implementation of the method, but rather contribute toward a better understanding of the approach potential. More precisely, we study different learning strategies relative to the models training datasets. Finally, we discuss the main advantages and limitations of DL4GD.

Published
2024
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25. Faster Edge‐Path Bundling through Graph Spanners.

Author

Wallinger, Markus, Archambault, Daniel, Auber, David, Nöllenburg, Martin, and Peltonen, Jaakko

Abstract

Edge‐Path bundling is a recent edge bundling approach that does not incur ambiguities caused by bundling disconnected edges together. Although the approach produces less ambiguous bundlings, it suffers from high computational cost. In this paper, we present a new Edge‐Path bundling approach that increases the computational speed of the algorithm without reducing the quality of the bundling. First, we demonstrate that biconnected components can be processed separately in an Edge‐Path bundling of a graph without changing the result. Then, we present a new edge bundling algorithm that is based on observing and exploiting a strong relationship between Edge‐Path bundling and graph spanners. Although the worst case complexity of the approach is the same as of the original Edge‐Path bundling algorithm, we conduct experiments to demonstrate that the new approach is 5–256 times faster than Edge‐Path bundling depending on the dataset, which brings its practical running time more in line with traditional edge bundling algorithms. [ABSTRACT FROM AUTHOR]

Published
2023
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26. Planar L-Drawings of Bimodal Graphs

Author

Angelini, Patrizio, Chaplick, Steven, Cornelsen, Sabine, Lozzo, Giordano Da, Auber, David, Valtr, Pavel, Auber D.,Valtr P., Angelini, Patrizio, Chaplick, Steven, Cornelsen, Sabine, DA LOZZO, Giordano, Chaplick, Steve, Dept. of Advanced Computing Sciences, RS: FSE DACS, RS: FSE DACS Mathematics Centre Maastricht, and DKE Scientific staff

Subjects
Computational Geometry (cs.CG), FOS: Computer and information sciences, Vertex (graph theory), 050101 languages & linguistics, General Computer Science, Discrete Mathematics (cs.DM), 02 engineering and technology, Computer Science::Computational Geometry, Edge (geometry), Theoretical Computer Science, Bimodality, Combinatorics, Planar, Computer Science::Discrete Mathematics, Computer Science - Data Structures and Algorithms, 0202 electrical engineering, electronic engineering, information engineering, Planar L-drawings, Data Structures and Algorithms (cs.DS), 0501 psychology and cognitive sciences, Mathematics, Plane (geometry), Directed graph, 05 social sciences, Digraph, Computer Science Applications, Computational Theory and Mathematics, Computer Science - Computational Geometry, Embedding, 020201 artificial intelligence & image processing, Geometry and Topology, Focus (optics), Computer Science - Discrete Mathematics
Abstract

In a planar L-drawing of a directed graph (digraph) each edge e is represented as a polyline composed of a vertical segment starting at the tail of e and a horizontal segment ending at the head of e. Distinct edges may overlap, but not cross. Our main focus is on bimodal graphs, i.e., digraphs admitting a planar embedding in which the incoming and outgoing edges around each vertex are contiguous. We show that every plane bimodal graph without 2-cycles admits a planar L-drawing. This includes the class of upward-plane graphs. Finally, outerplanar digraphs admit a planar L-drawing - although they do not always have a bimodal embedding - but not necessarily with an outerplanar embedding., Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)

Published
2022

30. Tulip 5

Author

Auber, David, primary, Archambault, Daniel, additional, Bourqui, Romain, additional, Delest, Maylis, additional, Dubois, Jonathan, additional, Lambert, Antoine, additional, Mary, Patrick, additional, Mathiaut, Morgan, additional, Melançon, Guy, additional, Pinaud, Bruno, additional, Renoust, Benjamin, additional, and Vallet, Jason, additional

Published
2017
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31. VRGrid: Efficient Transformation of 2D Data into Pixel Grid Layout

Author

Halnaut, Adrien, Giot, Romain, Bourqui, Romain, Auber, David, Halnaut, Adrien, and Élicitation interactive des contraintes pour la fouille de données non-supervisée et semi-supervisée - - InvolvD2020 - ANR-20-CE23-0023 - AAPG2020 - VALID

Subjects
[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], evaluation, compact visualization, visualization
Abstract

Projecting a set of n points on a grid of size √ n× √ n provides the best possible information density in two dimensions without overlap. We leverage the Voronoi Relaxation method to devise a novel and versatile post-processing algorithm called VRGrid: it enables the arrangement of any 2D data on a grid while preserving its initial positions. We apply VRGrid to generate compact and overlap-free visualization of popular and overlap-prone projection methods (e.g., t-SNE). We prove that our method complexity is O (√ n.i.n.log(n)), with i a determined maximum number of iterations and n the input dataset size. It is thus usable for visualization of several thousands of points. We evaluate VRGrid's efficiency with several metrics: distance preservation (DP), neighborhood preservation (NP), pairwise relative positioning preservation (RPP) and global positioning preservation (GPP). We benchmark VRGrid against two stateof-the-art methods: Self-Sorting Maps (SSM) and Distancepreserving Grid (DGrid). VRGrid outperforms these two methods, given enough iterations, on DP, RPP and GPP which we identify to be the key metrics to preserve the positions of the original set of points.

Published
2022

34. New Strahler Numbers for Rooted Plane Trees

Author

Auber, David, Domenger, Jean-Philippe, Delest, Maylis, Duchon, Philippe, Fédou, Jean-Marc, Drmota, Michael, editor, Flajolet, Philippe, editor, Gardy, Danièle, editor, and Gittenberger, Bernhard, editor

Published
2004
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35. Big data visualization and analytics: Future research challenges and emerging applications

Author

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Andrienko, Gennady, Adrienko, Natalia, Drucker, Steven, Fekete, Jean-Daniel, Fisher, Danyel, Idreos, Stavros, Kraska, Tim, Li, Guoliang, Ma, Kwan-Liu, Mackinlay, Jock D., Oulasvirta, Antti, Auber, David, Bikakis, Nikos, Chrysanthis, Panos K., Papastefanatos, George, Sharaf, Mohamed A., Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science, Andrienko, Gennady, Adrienko, Natalia, Drucker, Steven, Fekete, Jean-Daniel, Fisher, Danyel, Idreos, Stavros, Kraska, Tim, Li, Guoliang, Ma, Kwan-Liu, Mackinlay, Jock D., Oulasvirta, Antti, Auber, David, Bikakis, Nikos, Chrysanthis, Panos K., Papastefanatos, George, and Sharaf, Mohamed A.

Abstract

© 2020 Copyright for this paper by its author(s). In the context of data visualization and analytics, this report outlines some of the challenges and emerging applications that arise in the Big Data era. In particularly, fourteen distinguished scientists from academia and industry, and diverse related communities, i.e., Information Visualization, Human-Computer Interaction, Machine Learning, Data management & Mining, and Computer Graphics have been invited to express their opinions.

Published
2022

44. MuGDAD: Multilevel graph drawing algorithm in a distributed architecture

Author

Hinge, Antoine, Richer, Gaëlle, Auber, David, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Projet REQUEST: Programme Investissement d’Avenir Big Data - Cloud Computing topic - PIA O18062-645401, IADIS, and Hinge, Antoine

Subjects
Big Data, Apache Spark, [INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG], Computational geometry, Distributed computing, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Graph drawing, [INFO.INFO-CG] Computer Science [cs]/Computational Geometry [cs.CG], [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]
Abstract

International audience; This paper presents a multiparadigm force-directed graph drawing algorithm with horizontal scalability on distributed storage clusters. Adaptations of the classical force-directed scheme that function on a distributed environment are presented. This distributed force-directed scheme is associated with a distributed-compatible multilevel approach for a more efficient graph drawing algorithm. MuGDAD is compared in terms of layout quality and speed with other algorithms.

Published
2017

45. Régression ridge a noyau dans un graphe : Application au réseau de distribution d'eau potable

Author

Dumora, Christophe, Bigot, Jérémie, Couallier, Vincent, Auber, David, Leclerc, Cyril, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Centre recherche et développement (LyRE), and Lyonnaise des Eaux

Subjects
Graph network analysis, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], réseau de distribution d'eau potable, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], water distribution network, Analyse de graphe, regression ridge à noyau, Kernel Ridge Regression
Abstract

Papier présenté à la conférence JDS Avignon 2017 le 30 mai 2017; The aim of this paper is to present the methodology of statistical inference and prediction for processes defined on network graphs when those processes are only observed on a small number of nodes. The kernel regression approach presented here already raise the question of eigenvectors computation of a very large adjacency matrix.; Ce travail aborde le problème de l'inférence sur les nœuds d'un très grand graphe, représentant un réseau de distribution d'eau potable, à partir d'une observation partielle de quelques données, possiblement chronologiques, sur un faible nombre de nœuds. Nous utilisons une approche de prédiction par noyau reposant sur un estimateur pénalisé de type ridge qui soulève des problèmes d'analyse spectrale d'une très grande matrice creuse.

Published
2017

46. Results of IndexMed GRAIL Days 2016: How to use standards to build GRAphs and mIne data for environmentaL research? IndexMeed consortium for data mining in ecology

Author

David, Romain, Féral, Jean-Pierre, Archambeau, Anne-Sophie, Arnaud, Fanny, Auber, David, Bailly, Nicolas, Bernard, Loup, Blanpain, Cyrille, BRETON, Vincent, Couvet, Denis, Cohen-Nabeiro, Anna, Delavaud, Aurélie, Dias, Alrick, Gachet, Sophie, Goffaux, Robin, Gibert, Karina, Herrera, Manuel, Ienco, Dino, Julliard, Romain, Lecubin, Julien, Legre, Yannick, Leydet, Michelle, Lois, Grégoire, Méndez Muñoz, Víctor, Meunier, Jean-Charles, Mougenot, Isabelle, Pamerlon, Sophie, Raynal, Jean-Claude, Romier, Genevieve, Roux-Michollet, Dad, Specht, Alison, Surace, Christian, Tatoni, Thierry, Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), Patrimoine naturel (PatriNat), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Office français de la biodiversité (OFB), Environnement, Ville, Société (EVS), École normale supérieure de Lyon (ENS de Lyon)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-École Nationale des Travaux Publics de l'État (ENTPE)-École nationale supérieure d'architecture de Lyon (ENSAL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Hellenic Centre for Marine Research (HCMR), Étude des Civilisations de l'Antiquité : de la Préhistoire à Byzance (ARCHIMEDE), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Université Marc Bloch - Strasbourg II-Centre National de la Recherche Scientifique (CNRS), Institut Pythéas (OSU PYTHEAS), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Physique Corpusculaire - Clermont-Ferrand (LPC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Fondation pour la recherche sur la Biodiversité (FRB), Fondation pour la Recherche sur la Biodiversité, Universitat Politècnica de Catalunya [Barcelona] (UPC), Fouille de données environnementales (TATOO), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre d'Ecologie et des Sciences de la COnservation (CESCO), Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Department of Computer Architecture & Operating Systems (CAOS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), UMR 228 Espace-Dev, Espace pour le développement, Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA), Institut de Recherche pour le Développement (IRD), Laboratoire de Physique Théorique d'Orsay [Orsay] (LPT), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) with the support of RDA Europe, Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Diversité, évolution et écologie fonctionnelle marine (DIMAR), Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS), IRD-UMS PatriNat-GBIF, Environnement Ville Société (EVS), École normale supérieure - Lyon (ENS Lyon)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet [Saint-Étienne] (UJM)-École Nationale des Travaux Publics de l'État (ENTPE)-École nationale supérieure d'architecture de Lyon (ENSAL)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université Sciences et Technologies - Bordeaux 1-Université Bordeaux Segalen - Bordeaux 2, Hellenic Center for Marine Research (HCMR), Etude des Civilisations de l'Antiquité (UMR 7044), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut de Recherche pour le Développement (IRD), Muséum national d'Histoire naturelle (MNHN), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Aix Marseille Université (AMU)-Centre National d'Études Spatiales [Toulouse] (CNES), Université des Antilles (UA)-Université de Guyane (UG)-Université de Montpellier (UM)-Université de La Réunion (UR)-Avignon Université (AU)-Université de Perpignan Via Domitia (UPVD)-Institut de Recherche pour le Développement (IRD), Centre National de la Recherche Scientifique (CNRS)-Institut de recherche pour le développement [IRD] : UMR237-Aix Marseille Université (AMU)-Avignon Université (AU), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-École nationale supérieure d'architecture de Lyon (ENSAL)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Nationale des Travaux Publics de l'État (ENTPE)-Université Jean Monnet [Saint-Étienne] (UJM)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Lumière - Lyon 2 (UL2)-École normale supérieure - Lyon (ENS Lyon), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Centre National de la Recherche Scientifique (CNRS)-Université Marc Bloch - Strasbourg II-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN), Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École nationale supérieure d'architecture de Lyon (ENSAL)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, 13. Climate action, 11. Sustainability, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], [SDE.BE]Environmental Sciences/Biodiversity and Ecology, [SDE.ES]Environmental Sciences/Environmental and Society
Abstract

The 9th RDA Plenary Meeting has taken place from 5th to 7th April 2017 at the Barcelo Sants Hotel, Barcelona, Spain.Organised under the theme “Data Infrastructures for Open Science”, the event will bring together an international multi-disciplinary audience of data scientists, researchers, industry leaders, entrepreneurs, policymakers and data stewards. Plenary meetings and networking opportunities are all focused on exploring the best ways to exploit the data revolution to improve science and society through data-driven discovery and innovation.Plenary programme highlights75+ Working and Interest Group meetingsPlenary Sessions on RDA Recommendations and AdoptionLeading Industry Panel discussionsRDA Recommendation demos and networking opportunitiesThe plenary meeting is organised by the Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS) with the support of RDA Europe.; International audience; Data produced by biodiversity research projects that evaluate and monitor Good Environmental Status have a high potential for use by stakeholders involved in [marine] environmental management. The lack of specific scientific objectives, poor organizational logic, and a characteristically disorganized collection of information leads to a decentralized data distribution, hampering environmental research. In such a heterogeneous system across different organizations and data formats, it is difficult to efficiently harmonize the outputs. There are few tools available to assist. The task of the newly created consortium of IndexMeed is to index biodiversity data (and to provide an index of qualified existing open datasets) and make it possible to build graphs to assist in the analysis and development of new ways to mine data. Standards (including TDWG recommendations) and specific protocols can be applied to interconnect databases. Such semantic approaches greatly increase data interoperability. The aim of this poster is to present the 2016 IndexMed workshop results (https://indexmed2016.sciencesconf.org) and recent actions of the consortium (renamed IndexMeed - Indexing for Mining Ecological and Environmental Data): new approaches to investigate complex research questions and support the emergence of new scientific hypotheses. With one day of plenary sessions and two days of practical workshops, this event was dedicated to the sharing of experience and expertise, the acquisition of practical methods to construct graphs and value data through metadata and ”data papers”. Recent developments in data mining based on graphs, the potential for important contributions to environmental research, particularly about strategic decision-making, and new ways of organizing data were also discussed at the workshop. In particular, this workshop promoted decisions on how (i) to analyze heterogeneous distributed data spread in different databases, (ii) to create matches and incorporate some approximations, (iii) to identify statistical relationships between observed data and the emergence of contextual patterns, and (iv) to encourage openness and the sharing of data, in order to value data and their utilization. The IndexMeed project participants are now exploring the ability of two scientific communities (ecology sensu lato and computer sciences) to work together. The uses of data from biodiversity research demonstrate the prototype functionalities and introduce new perspectives to analyze environmental and societal responses including decision-making. Output of the seminar lists scientific questions that can be resolved by the new data mining approaches and proposes new ways to investigate heterogeneous environmental data with graph mining.

Published
2017
Full Text
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48. TULIP 4

Author

Auber, David, Bourqui, Romain, Delest, Maylis, Lambert, Antoine, Mary, Patrick, Melançon, Guy, Pinaud, Bruno, Renoust, Benjamin, Vallet, Jason, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB), Thales Services, THALES, Japanese French Laboratory for Informatics (JFLI), Centre National de la Recherche Scientifique (CNRS)-The University of Tokyo (UTokyo)-Université Pierre et Marie Curie - Paris 6 (UPMC)-National Institute of Informatics (NII), National Institute of Informatics (NII), and LaBRI - Laboratoire Bordelais de Recherche en Informatique

Subjects
[INFO]Computer Science [cs]
Abstract

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on more than 15 years of research and development, Tulip is built on a suite of tools and techniques , that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide Python and/or C++ developers a complete library, supporting the design of interactive information visualization applications for relational data, that can be customized to address a wide range of visualization problems. In its current iteration, Tulip enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. This development pipeline makes the framework efficient for creating research prototypes as well as developing end-user applications. The recent addition of a complete Python programming layer wraps up Tulip as an ideal tool for fast prototyping and treatment automation, allowing to focus on problem solving, and as a great system for teaching purposes at all education levels.

Published
2016

49. Results of IndexMed GRAIL Days 2016: How to use standards to build GRAphs and mIne data for environmentaL research?

Author

David, Romain, Féral, Jean-Pierre, Anne-Sophie Archambeau, Auber, David, Bailly, Nicolas, Blanpain, Cyrille, Breton, Vincent, Dias, Alrick, Cohen-Nabeiro, Anna, Delavaud, Aurélie, Gachet, Sophie, Goffaux, Robin, Gibert, Karina, Herrera, Manuel, Ienco, Dino, Julliard, Romain, Lecubin, Julien, Legre, Yannick, Loïs, Grégoire, Muñoz, Victor Méndez, Jean-Charles Meunier, Mougenot, Isabelle, Pamerlon, Sophie, Romier, Geneviève, Specht, Alison, Surace, Christian, and Tatoni, Thierry

Published
2016
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50. IndexMEED cases studies using "Omics" data with graph theory

Author

David, Romain, primary, Féral, Jean-Pierre, additional, Archambeau, Anne-Sophie, additional, Arnaud, Fanny, additional, Auber, David, additional, Bailly, Nicolas, additional, Bernard, Loup, additional, Berti-Equille, Laure, additional, Blanpain, Cyrille, additional, Breton, Vincent, additional, Chenuil-Maurel, Anne, additional, Cohen Nabeiro, Anna, additional, Dias, Alrick, additional, Delavaud, Aurélie, additional, Goffaud, Robin, additional, Gachet, Sophie, additional, Gibert, Karina, additional, Herrera Fernandez, Manuel, additional, Hogie, Luc, additional, Ienco, Dino, additional, Julliard, Romain, additional, Le Bras, Yvan, additional, Lecubin, Julien, additional, Legre, Yannick, additional, Leydet, Michelle, additional, Lois, Grégoire, additional, Madon, Bénédicte, additional, Marchal, François, additional, Mendez Munoz, Victor, additional, Meunier, Jean-Charles, additional, Mihoub, Jean-Baptiste, additional, Mougenot, Isabelle, additional, Pamerlon, Sophie, additional, Peletier, Eric, additional, Romier, Geneviève, additional, Roux-Michollet, Dad, additional, Specht, Alison, additional, Surace, Christian, additional, Raynal, Jean-Claude, additional, and Tatoni, Thierry, additional

Published
2017
Full Text
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