31,317 research outputs found

    Towards Scalable Visual Exploration of Very Large RDF Graphs

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    In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.Comment: 12th Extended Semantic Web Conference (ESWC 2015

    StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices

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    Given a large-scale graph with millions of nodes and edges, how to reveal macro patterns of interest, like cliques, bi-partite cores, stars, and chains? Furthermore, how to visualize such patterns altogether getting insights from the graph to support wise decision-making? Although there are many algorithmic and visual techniques to analyze graphs, none of the existing approaches is able to present the structural information of graphs at large-scale. Hence, this paper describes StructMatrix, a methodology aimed at high-scalable visual inspection of graph structures with the goal of revealing macro patterns of interest. StructMatrix combines algorithmic structure detection and adjacency matrix visualization to present cardinality, distribution, and relationship features of the structures found in a given graph. We performed experiments in real, large-scale graphs with up to one million nodes and millions of edges. StructMatrix revealed that graphs of high relevance (e.g., Web, Wikipedia and DBLP) have characterizations that reflect the nature of their corresponding domains; our findings have not been seen in the literature so far. We expect that our technique will bring deeper insights into large graph mining, leveraging their use for decision making.Comment: To appear: 8 pages, paper to be published at the Fifth IEEE ICDM Workshop on Data Mining in Networks, 2015 as Hugo Gualdron, Robson Cordeiro, Jose Rodrigues (2015) StructMatrix: Large-scale visualization of graphs by means of structure detection and dense matrices In: The Fifth IEEE ICDM Workshop on Data Mining in Networks 1--8, IEE

    Using treemaps for variable selection in spatio-temporal visualisation

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    We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km(2) area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time- based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation. Information Visualization (2008) 7, 210-224. doi: 10.1057/palgrave.ivs.950018

    Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos

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    What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessible for regular users, that leverage these theoretical results. HIPie -- an interactive visualization system -- is created to fill this gap, by enabling users to reason about the virality and the popularity of online videos. It retrieves the metadata and the past popularity series of Youtube videos, it employs Hawkes Intensity Process, a state-of-the-art online popularity model for explaining and predicting video popularity, and it presents videos comparatively in a series of interactive plots. This system will help both content consumers and content producers in a range of data-driven inquiries, such as to comparatively analyze videos and channels, to explain and predict future popularity, to identify viral videos, and to estimate response to online promotion.Comment: 4 page

    Interactive tag maps and tag clouds for the multiscale exploration of large spatio-temporal datasets

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    'Tag clouds' and 'tag maps' are introduced to represent geographically referenced text. In combination, these aspatial and spatial views are used to explore a large structured spatio-temporal data set by providing overviews and filtering by text and geography. Prototypes are implemented using freely available technologies including Google Earth and Yahoo! 's Tag Map applet. The interactive tag map and tag cloud techniques and the rapid prototyping method used are informally evaluated through successes and limitations encountered. Preliminary evaluation suggests that the techniques may be useful for generating insights when visualizing large data sets containing geo-referenced text strings. The rapid prototyping approach enabled the technique to be developed and evaluated, leading to geovisualization through which a number of ideas were generated. Limitations of this approach are reflected upon. Tag placement, generalisation and prominence at different scales are issues which have come to light in this study that warrant further work

    Serving GODAE Data and Products to the Ocean Community

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    The Global Ocean Data Assimilation Experiment (GODAE [http:// www.godae.org]) has spanned a decade of rapid technological development. The ever-increasing volume and diversity of oceanographic data produced by in situ instruments, remote-sensing platforms, and computer simulations have driven the development of a number of innovative technologies that are essential for connecting scientists with the data that they need. This paper gives an overview of the technologies that have been developed and applied in the course of GODAE, which now provide users of oceanographic data with the capability to discover, evaluate, visualize, download, and analyze data from all over the world. The key to this capability is the ability to reduce the inherent complexity of oceanographic data by providing a consistent, harmonized view of the various data products. The challenges of data serving have been addressed over the last 10 years through the cooperative skills and energies of many individuals
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