The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets

Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s$\cdot$he wants to explore other facets of a multi-facetted information space. For instance, in a multimedia dataset different facets such as keywords, authors, publication category, organisations and figures can be of interest. The facet simultaneous visualisation can help to gain insights on the information retrieved and call for further searches. Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of multisets -- and are in fact linked not only to the publication itself, but to any chosen reference. These references allow to navigate inside the dataset and perform visual queries. We explore here the case of scientific publications based on Arxiv searches.Comment: Extension of the hypergraph framework shortly presented in arXiv:1809.00164 (possible small overlaps); use the theoretical framework of hb-graphs presented in arXiv:1809.0019

A study of navigation strategies in spatial-semantic visualizations

Visualisations of abstract data are believed to assist the searcher by providing an overview of the semantic structure of a document collection whereby semantically similar items tend to cluster in space. Cribbin and Chen (2001) found that similarity data represented using minimum spanning tree (MST) graphs provided greater levels of support to users when conducting a range of information seeking tasks, in comparison to simple scatter graphs. MST graphs emphasise the most salient relationships between nodes by means of connecting links. This paper is based on the premise that it is the provision of these links that facilitated search performance. Using a combination of visual observations and existing theory, hypotheses predicting navigational strategies afforded by the MST link structure are presented and tested. The utility, in terms of navigational efficiency and retrieval success, of these and other observed strategies is then examined

Posing 3D Models from Drawing

Inferring the 3D pose of a character from a drawing is a complex and under-constrained problem. Solving it may help automate various parts of an animation production pipeline such as pre-visualisation. In this paper, a novel way of inferring the 3D pose from a monocular 2D sketch is proposed. The proposed method does not make any external assumptions about the model, allowing it to be used on different types of characters. The inference of the 3D pose is formulated as an optimisation problem and a parallel variation of the Particle Swarm Optimisation algorithm called PARAC-LOAPSO is utilised for searching the minimum. Testing in isolation as well as part of a larger scene, the presented method is evaluated by posing a lamp, a horse and a human character. The results show that this method is robust, highly scalable and is able to be extended to various types of models

PORGY: a Visual Analytics Platform for System Modelling and Analysis Based on Graph Rewriting

PORGY is a visual environment for rule-based modelling based on port graphs and port graph rewrite rules whose application is steered by rewriting strategies. The focus of this demonstration is the visual and interactive features offered by PORGY, which facilitate an exploratory approach to model, simu- late and analyse different ways of applying the rules while recording the model evolution, as well as tracking and plotting system parameters

Exploring cognitive issues in visual information retrieval

A study was conducted that compared user performance across a range of search tasks supported by both a textual and a visual information retrieval interface (VIRI). Test scores representing seven distinct cognitive abilities were examined in relation to user performance. Results indicate that, when using VIRIs, visual-perceptual abilities account for significant amounts of within-subjects variance, particularly when the relevance criteria were highly specific. Visualisation ability also seemed to be a critical factor when users were required to change topical perspective within the visualisation. Suggestions are made for navigational cues that may help to reduce the effects of these individual differences

An environment for studying the impact of spatialising sonified graphs on data comprehension

We describe AudioCave, an environment for exploring the impact of spatialising sonified graphs on a set of numerical data comprehension tasks. Its design builds on findings regarding the effectiveness of sonified graphs for numerical data overview and discovery by visually impaired and blind students. We demonstrate its use as a test bed for comparing the approach of accessing a single sonified numerical datum at a time to one where multiple sonified numerical data can be accessed concurrently. Results from this experiment show that concurrent access facilitates the tackling of our set multivariate data comprehension tasks. AudioCave also demonstrates how the spatialisation of the sonified graphs provides opportunities for sharing the representation. We present two experiments investigating users solving set data comprehension tasks collaboratively by sharing the data representation

Using domain models for context-rich user logging

This paper describes the prototype interactive search sys- Tem being developed within the AutoAdapt project1. The AutoAdapt project seeks to enhance the user experience in searching for information and navigating within selected do- main collections by providing structured representations of domain knowledge to be directly explored, logged, adapted and updated to refject user needs. We propose that this structure is a valuable stepping-stone in context-rich logging of user activities within the information seeking environment. Here we describe the primary components that have been implemented and the user interactions that it will support

Towards Scalable Visual Exploration of Very Large RDF Graphs

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