Visualisation of Linked Data – Reprise

Linked Data promises to serve as a disruptor of traditional approaches to data management and use, promoting the push from the traditional Web of documents to a Web of data. The ability for data consumers to adopt a follow your nose approach, traversing links defined within a dataset or across independently-curated datasets, is an essential feature of this new Web of Data, enabling richer knowledge retrieval thanks to synthesis across multiple sources of, and views on, inter-related datasets. But for the Web of Data to be successful, we must design novel ways of interacting with the corresponding very large amounts of complex, interlinked, multi-dimensional data throughout its management cycle. The design of user interfaces for Linked Data, and more specifically interfaces that represent the data visually, play a central role in this respect. Contributions to this special issue on Linked Data visualisation investigate different approaches to harnessing visualisation as a tool for exploratory discovery and basic-to-advanced analysis. The papers in this volume illustrate the design and construction of intuitive means for end-users to obtain new insight and gather more knowledge, as they follow links defined across datasets over the Web of Data

Big Data Visualization Tools

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sense-making activities.Comment: This article appears in Encyclopedia of Big Data Technologies, Springer, 201

Embodied conversations: Performance and the design of a robotic dancing partner

This paper reports insights gained from an exploration of performance-based techniques to improve the design of relationships between people and responsive machines. It draws on the Emergent Objects project and specifically addresses notions of embodiment as employed in the field of performance as a means to prototype and develop a robotic agent, SpiderCrab, designed to promote expressive interaction of device and human dancer, in order to achieve ‘performative merging’. The significance of the work is to bring further knowledge of embodiment to bear on the development of human-technological interaction in general. In doing so, it draws on discursive and interpretive methods of research widely used in the field of performance but not yet obviously aligned with some orthodox paradigms and practices within design research. It also posits the design outcome as an ‘objectile’ in the sense that a continuous and potentially divergent iteration of prototypes is envisaged, rather than a singular final product. The focus on performative merging draws in notions of complexity and user experience. Keywords: Embodiment; Performance; Tacit Knowledge; Practice-As-Research; Habitus.</p

Lignes du temps connectées pour l’analyse visuelle d’adaptations et de relations entre artistes

Le récent essor des données liées ouvertes démocratise l’accès à des jeux de données qui prennent souvent la forme de graphes, complexes à comprendre sans support visuel adapté. La visualisation de graphes est un sujet de recherche encore très actif. Bien qu’il existe aujourd’hui de nombreuses techniques de visualisation, dès qu’un graphe atteint une certaine grandeur (problème d’échelle) et une certaine quantité d’attributs (problème de densité d’informations) les techniques existantes perdent en efficacité. Lors d’un récent projet d’innovation en valorisation des données avec BAnQ, nous avons travaillé avec un vaste jeu de données sur les adaptations musicales. L’objectif était d’arriver à représenter les relations de collaboration et d’influence entre les artistes tout en restant lisible. Les techniques de visualisation de graphes existantes ne permettaient pas de réaliser ces tâches avec suffisamment de clarté, ce qui nous a amené à proposer une nouvelle méthode de visualisation nommée MuzLink. L’objectif de ce travail de recherche est la conception, le développement et l’évaluation d’une nouvelle méthode de visualisation de graphes bipartis multivariés. La nouvelle méthode de visualisation a d’abord nécessité la création d’un nouveau glossaire musical pour communiquer sans ambiguïté les différents rôles et relations qui existent entre artistes et chansons. La méthode de visualisation proposée est centrée sur un seul artiste. Elle s’inspire de plusieurs techniques existantes (node-link, lignes du temps et beeswarm) et les combine afin de représenter de façon explicite l’évolution des relations de collaboration et d’influence dans le temps. Cette méthode est divisée en trois lignes du temps : la première contient les chansons produites par l’artiste, la deuxième contient les chansons qui ont inspiré l’artiste et la troisième les chansons qui s’inspirent de productions de l’artiste. Chaque relation d’influence entre chansons est représentée par un lien, créant du même coup des lignes du temps connectées. Cette visualisation principale est juxtaposée à trois autres outils complémentaires qui permettent de répondre à des tâches spécifiques (listes d’artistes, notice de chanson et résumé de l’artiste). Les listes d’artistes énumèrent les artistes qui ont inspiré, collaboré ou se sont inspirés de l’artiste. La notice de chanson affiche des informations supplémentaires sur une chanson sélectionnée. Le résumé de l’artiste donne une vue globale sur les rôles occupés et les types de productions de l’artiste.----------ABSTRACT: The rise of Linked Open Data in the recent years is emocratizing access to complex data structures, usually taking the form of very large multivariate graphs. These are typically hard to understand for humans and require the use of visualization tools. While this data structure is a very well-researched topic in data visualization, large and complex graphs — with several heterogeneous node and link attributes — are hard to visualize with existing techniques. During a project in collaboration with the national library and archives agency of the Quebec government, we have been mandated to visualize a complex dataset of relationships between artists. Eleven key exploratory tasks were selected during the initial design process. Standard graph visualization techniques weren’t suited to answers those specific tasks. Furthermore, since the dataset’s structure is an unusual combination of a bipartite graph and trees, a thorough literature review found very few related or similar work. This research proposes a novel visualization method called MuzLink to efficiently explore and discover relationships between artists. The objective is to evaluate the effectiveness of this method for the selected task with a formal user study. Based on all eleven selected tasks, a fully functional prototype was developed. The proposed solution is a multi-facet visualization tool centered around a specific artist. The first view contains 3 connected timelines: (1) the inspirations, (2) the productions, and (3) the influences. Beeswarms of songs are positioned on the timelines according to their relationship with the artist. Songs on the middle timeline are the artist’s production. Songs that inspired the artist are on the top timeline. Songs inspired by the artist are on the bottom timeline. Links are drawn between songs having an influence relationships. This connected timeline allows a topological exploration of inspiration relationships around an artist’s musical production. The second view shows inspired, collaborators, and influenced artists ordered by their number of implications with the current artist. Interactions between the two complementary views allow answering a larger range of tasks. The user can easily navigate between artists with a search bar and by clicking on related artists. Clicking on a song reveals more details regarding the relationships among other songs

Huma-Num. La TGIR des humanités numériques. Rapport d'activité 2013-2015

Huma-Num (UMS 3598) est une très grande infrastructure (TGIR) née de la fusion du TGE-Adonis et de IR-Corpus. Elle vise à faciliter le tournant numérique de la recherche en sciences humaines et sociales. Pour remplir cette mission, la TGIR Huma-Num est bâtie sur une organisation originale consistant à mettre en oeuvre un dispositif humain (concertation collective) et technologique (services numériques pérennes) à l’échelle nationale et européenne en s’appuyant sur un important réseau de partenaires et d’opérateurs. Ce rapport intermédiaire dresse le bilan des premières années de la TGIR en rappelant son contexte de création, les services créées, son fonctionnement en réseau et les perspectives de développement à moyen terme

A fruitful fly forward : the role of the fly in drug discovery for neurodegeneration

AD, Alzheimer’s disease; APP, amyloid precursor protein; BBB, blood brain barrier; GFP, green fluorescent protein; HTS, high-throughput screening; HD, Huntington’s disease; LB, Lewy bodies; PD, Parkinson’s disease; PolyQ, Polyglutamine; RNAi, RNA interference; SNCA, α-synuclein gene; UAS, Upstream Activating Sequence.peer-reviewe

Combining Faceted Search with Data-analytic Visualizations on Top of a SPARQL Endpoint

This paper discusses practical experiences on creating data-analytic visualizations in a browser, on top of a SPARQL endpoint based on the results of faceted search. Four use cases related to Digital Humanities research in proposography are discussed where the SPARQL Faceter tool was used and extended in different ways. The Faceter tool allows the user to select a group of people with shared properties, e.g., people with the same place of birth, gender, profession, or employer. The filtered data can then be visualized, e.g., as column charts, with business graphics, sankey diagrams, or on a map. The use cases examine the potential of visualization as well as automated knowledge discovery in Digital Humanities research.Peer reviewe

A comparative study of state-of-the-art linked data visualization tools

Data visualization tools are of great importance for the exploration and the analysis of Linked Data (LD) datasets. Such tools allow users to get an overview, understand content, and discover interesting insights of a dataset. Visualization approaches vary according to the domain, the type of data, the task that the user is trying to perform, as well as the skills of the user. Thus, the study of the capabilities that each approach offers is crucial in supporting users to select the proper tool/technique based on their need. In this paper we present a comparative study of the state-of-the-art LD visualization tools over a list of fundamental use cases. First, we define 16 use cases that are representative in the setting of LD visual exploration, examining several tool's aspects; e.g., functionality capabilities, feature richness. Then, we evaluate these use cases over 10 LD visualization tools, examining: (1) if the tools have the required functionality for the tasks; and (2) if they allow the successful completion of the tasks over the DBpedia dataset. Finally, we discuss the insights derived from the evaluation, and we point out possible future directions