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Time filter

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Roubaix, France

Liu T.,University of Tours | Bouali F.,University of Lille2 | Venturini G.,University of Tours
Distributed and Parallel Databases | Year: 2015

In this paper, we study how to visualize large amounts of multidimensional data with a radial visualization. For such a visualization, we study a multi-threaded implementation on the CPU and the GPU. We start by reviewing the approaches that have visualized the largest multidimensional datasets and we focus on the approaches that have used CPU or GPU parallelization. We consider the radial visualizations and we describe our approach (called POIViz) that uses points of interest to determine a layout of a large dataset. We detail its parallelization on the CPU and the GPU. We study the efficiency of this approach with different configurations and for large datasets. We show that it can visualize, in less than one second, millions of data with tens of dimensions, and that it can support “real-time” interactions even for large datasets. We conclude on the advantages and limits of the proposed visualization. © 2015 Springer Science+Business Media New York Source


Bouali F.,University of Lille2 | Bouali F.,University of Tours | Guettala A.,University of Tours | Venturini G.,University of Tours
Visual Computer | Year: 2015

We study in this work how a user can be guided to find a relevant visualization in the context of visual data mining. We present a state of the art on the user assistance in visual and interactive methods. We propose a user assistant called VizAssist, which aims at improving the existing approaches along three directions: it uses simpler computational models of the visualizations and the visual perception guidelines, in order to facilitate the integration of new visualizations and the definition of a mapping heuristic. VizAssist allows the user to provide feedback in a visual and interactive way, with the aim of improving the data to visualization mapping. This step is performed with an interactive genetic algorithm. Finally, VizAssist aims at proposing a free on-line tool (www.vizassist.fr) that respects the privacy of the user data. This assistant can be viewed as a global interface between the user and some of the many visualizations that are implemented with D3js. © 2015 Springer-Verlag Berlin Heidelberg Source


Bouali F.,University of Lille2 | Devaux S.,Airbus | Venturini G.,University of Tours
Visual Computer | Year: 2016

In this paper, we study the visual mining of time series, and we contribute to the study and evaluation of 3D tubular visualizations. We describe the state of the art in the visual mining of time-dependent data, and we concentrate on visualizations that use a tubular shape to represent data. After analyzing the motivations for studying such a representation, we present an extended tubular visualization. We propose new visual encodings of the time and data, new interactions for knowledge discovery, and the use of rearrangement clustering. We show how this visualization can be used in several real-world domains and that it can address large datasets. We present a comparative user study. We conclude with the advantages and the drawbacks of our method (especially the tubular shape). © 2014, Springer-Verlag Berlin Heidelberg. Source


Kamath N.,Manipal University India | Pai C.,Manipal University India | Deltombe T.,University of Lille2
Indian Journal of Pharmacology | Year: 2016

Various mechanisms contribute to anemia in inflammatory bowel diseases (IBD), drug-related causes being less frequent. The hematological and other adverse events of azathioprine (AZA) therapy are well documented, but drug-associated pure red cell aplasia (PRCA) is an uncommon event. We hereby describe two cases of AZA-associated PRCA in patients with Crohn's disease. The diagnosis was supported by pathological reports, and prompt hematological recovery was seen with discontinuation of the offending drug. This report highlights the need to consider this rare entity in IBD patients in appropriate settings and for adopting adequate precautionary measures. © 2016 Indian Journal of Pharmacology Published by Wolters Kluwer - Medknow. Source


Liu T.,University of Tours | Bouali F.,University of Tours | Bouali F.,University of Lille2 | Venturini G.,University of Tours
Proceedings of the International Conference on Information Visualisation | Year: 2015

We study in this paper the visualization of large multidimensional datasets with a focus on Open Data. Starting from our early work in which we defined a visualization based on points of interest, we improve this method in several ways with the aim of dealing with larger datasets and especially Open datasets. We propose the parallelization, using CPU and GPU, of the most costly steps of our method, like the computation of the data layout. We improve the visualization with a density rendering so as to keep the display informative for large datasets and for Open Data. We propose a layered visualization with interactions that can support several users tasks such as data filtering and labeling. We show that, even with common hardware, the performances of our approach are such that any user graphical queries can be processed in a few seconds. We detail how we were able to visualize and explore a collection of 300,000 Open datasets from the French Open Data web site. With the resulting visualization, we were able to improve our previous results. © 2015 IEEE. Source

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