Lost in translation: data integration tools meet the Semantic Web (experiences from the Ondex project)

More information is now being published in machine processable form on the web and, as de-facto distributed knowledge bases are materializing, partly encouraged by the vision of the Semantic Web, the focus is shifting from the publication of this information to its consumption. Platforms for data integration, visualization and analysis that are based on a graph representation of information appear first candidates to be consumers of web-based information that is readily expressible as graphs. The question is whether the adoption of these platforms to information available on the Semantic Web requires some adaptation of their data structures and semantics. Ondex is a network-based data integration, analysis and visualization platform which has been developed in a Life Sciences context. A number of features, including semantic annotation via ontologies and an attention to provenance and evidence, make this an ideal candidate to consume Semantic Web information, as well as a prototype for the application of network analysis tools in this context. By analyzing the Ondex data structure and its usage, we have found a set of discrepancies and errors arising from the semantic mismatch between a procedural approach to network analysis and the implications of a web-based representation of information. We report in the paper on the simple methodology that we have adopted to conduct such analysis, and on issues that we have found which may be relevant for a range of similar platformsComment: Presented at DEIT, Data Engineering and Internet Technology, 2011 IEEE: CFP1113L-CD

AMADA-Analysis of Multidimensional Astronomical Datasets

We present AMADA, an interactive web application to analyse multidimensional datasets. The user uploads a simple ASCII file and AMADA performs a number of exploratory analysis together with contemporary visualizations diagnostics. The package performs a hierarchical clustering in the parameter space, and the user can choose among linear, monotonic or non-linear correlation analysis. AMADA provides a number of clustering visualization diagnostics such as heatmaps, dendrograms, chord diagrams, and graphs. In addition, AMADA has the option to run a standard or robust principal components analysis, displaying the results as polar bar plots. The code is written in R and the web interface was created using the Shiny framework. AMADA source-code is freely available at https://goo.gl/KeSPue, and the shiny-app at http://goo.gl/UTnU7I.Comment: Accepted for publication in Astronomy & Computin

Analysis of web visit histories, part I: Distance-based visualization of sequence rules

This paper constitutes Part I of the contribution to the analysis of web visit histories through a new methodological framework. Firstly, web usage and web structure mining are considered as an unique mining process to detect the latent structure of the web navigation across the web sections of a single portal. We extend association rules theory to web data defining new concepts of web (patterns) association and preference matrices, as well as of (indirect and direct) sequence rules. We identify the most significant rules, according to a multiple testing procedure. In the literature, web usage patterns can be visualized in no-distance-based graphs describing the navigation behavior across web pages with sequential arrows. In the following, we introduce a geometrical visualization of sequence rules at any click of the web navigation. In particular, we provide two distance-based visualization methods for the static analysis of all data tout court and the dynamic analysis to discover the most significant web paths click by click. A real world case study is considered throughout the methodological description

Data Visualization in the Web-based Platform: Comparisons of Different Techniques / Data formats that Suit Web Information Design and Their Development

Data Visualization has become an important way of visual communication today with the assistance of different media. The computer, because of its interactive features, has become a useful tool to help people analyze data, design visualized and interactive graphs and publish data visualization work for the public to view. The main goal of this research is to provide an overview of the development of data visualization techniques especially in the web platform. An analysis result was generated to give future technique development suggestions for both web-based data visualization producers and web developers.Master of Science in Information Scienc

Recherche et représentation de communautés dans des grands graphes

15 pagesNational audienceThis paper deals with the analysis and the visualization of large graphs. Our interest in such a subject-matter is related to the fact that graphs are convenient widespread data structures. Indeed, this type of data can be encountered in a growing number of concrete problems: Web, information retrieval, social networks, biological interaction networks... Furthermore, the size of these graphs becomes increasingly large as the progression of the means for data gathering and storage steadily strengthens. This calls for new methods in graph analysis and visualization which are now important and dynamic research fields at the interface of many disciplines such as mathematics, statistics, computer science and sociology. In this paper, we propose a method for graphs representation and visualization based on a prior clustering of the vertices. Newman and Girvan (2004) points out that “reducing [the] level of complexity [of a network] to one that can be interpreted readily by the human eye, will be invaluable in helping us to understand the large-scale structure of these new network data”: we rely on this assumption to use a priori a clustering of the vertices as a preliminary step for simplifying the representation of the graphs - as a whole. The clustering phase consists in optimizing a quality measure specifically suitable for the research of dense groups in graphs. This quality measure is the modularity and expresses the “distance” to a null model in which the graph edges do not depend on the clustering. The modularity has shown its relevance in solving the problem of uncovering dense groups in a graph. Optimization of the modularity is done through a stochastic simulated annealing algorithm. The visualization/representation phase, as such, is based on a force-directed algorithm described in Truong et al. (2007). After giving a short introduction to the problem and detailing the vertices clustering and representation algorithms, the paper will introduce and discuss two applications from the social network field

Chisio Web : a web-based framework for customizable visualization of relational information

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical references leaves 89-92.Graphs are widely used to represent complex relational information. Graph visualization is crucial for effective analysis of information. In simple graphs, nodes are generally considered as uniform-sized components and they cannot be nested. This is often not sufficient to visualize complex relationships, because relational information is often clustered or hierarchically organized into groups or nested structures. There exist many free, open source software in the field of web-based graph visualization. However, none fully supports compound or clustered graphs. Moreover, customization provided by such software is often limited to the basic visual properties of nodes and edges. It requires a lot of effort to build an advanced customization of visual properties and interactive functionality with these software. In this thesis, we introduce a free, open source, general-purpose, web-based graph visualization framework, named Chisio Web (ChiWeb). ChiWeb supports visualization, interactive editing and layout of both simple and compound graphs. ChiWeb is implemented in ActionScript language and based on Flare, which is an open source ActionScript library designed for data visualization. ChiWeb is specifically designed for easy customization with respect to visualization and functionality. ChiWeb can be used as a library to create a custom graph visualization with an advanced application behavior for particular needs of a specific domain. The elements and functionality that can be easily customized with ChiWeb are: visual styles, controls for interactive events such as node creation, key and mouse functionality, context menus, toolbars, and inspector windows. Furthermore, ChiWeb’s architecture allows easy integration of new graph layout algorithms.Sümer, Selçuk OnurM.S

Argo Lite: Open-Source Interactive Graph Exploration and Visualization in Browsers

Graph data have become increasingly common. Visualizing them helps people better understand relations among entities. Unfortunately, existing graph visualization tools are primarily designed for single-person desktop use, offering limited support for interactive web-based exploration and online collaborative analysis. To address these issues, we have developed Argo Lite, a new in-browser interactive graph exploration and visualization tool. Argo Lite enables users to publish and share interactive graph visualizations as URLs and embedded web widgets. Users can explore graphs incrementally by adding more related nodes, such as highly cited papers cited by or citing a paper of interest in a citation network. Argo Lite works across devices and platforms, leveraging WebGL for high-performance rendering. Argo Lite has been used by over 1,000 students at Georgia Tech's Data and Visual Analytics class. Argo Lite may serve as a valuable open-source tool for advancing multiple CIKM research areas, from data presentation, to interfaces for information systems and more.Comment: CIKM'20 Resource Track (October 19-23, 2020), 6 pages, 6 figure