GraphDiaries: Animated Transitions and Temporal Navigation for Dynamic Networks

International audienceIdentifying, tracking and understanding changes in dynamic networks are complex and cognitively demanding tasks. We present GraphDiaries, a visual interface designed to improve support for these tasks in any node-link based graph visualization system. GraphDiaries relies on animated transitions that highlight changes in the network between time steps, thus helping users identify and understand those changes. To better understand the tasks related to the exploration of dynamic networks, we first introduce a task taxonomy, that informs the design of GraphDiaries, presented afterwards. We then report on a user study, based on representative tasks identified through the taxonomy, and that compares GraphDiaries to existing techniques for temporal navigation in dynamic networks, showing that it outperforms them in terms of both task time and errors for several of these tasks

The State of the Art in Multilayer Network Visualization

Modelling relationship between entities in real-world systems with a simple graph is a standard approach. However, realityis better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model hasemerged from the field of complex systems. This model can be applied to a wide range of real-world data sets. Examples ofmultilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domainof graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs.This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only forresearchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as wellas those developing systems across application domains. We have explored the visualization literature to survey visualizationtechniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within applicationdomains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future researchdirections for addressing them

Supporting Tools for Automated Generation and Visual Editing of Relational-to-Ontology Mappings

La integració de dades amb formats heterogenis i de diversos dominis mitjançant tecnologies de la web semàntica permet solucionar la seva disparitat estructural i semàntica. L'accés a dades basat en ontologies (OBDA, en anglès) és una solució integral que es basa en l'ús d'ontologies com esquemes mediadors i el mapatge entre les dades i les ontologies per facilitar la consulta de les fonts de dades. No obstant això, una de les principals barreres que pot dificultar més l'adopció de OBDA és la manca d'eines per donar suport a la creació de mapatges entre dades i ontologies. L'objectiu d'aquesta investigació ha estat desenvolupar noves eines que permetin als experts sense coneixements d'ontologies la creació de mapatges entre dades i ontologies. Amb aquesta finalitat, s'han dut a terme dues línies de treball: la generació automàtica de mapatges entre dades relacionals i ontologies i l'edició dels mapatges a través de la seva representació visual. Les eines actualment disponibles per automatitzar la generació de mapatges estan lluny de proporcionar una solució completa, ja que es basen en els esquemes relacionals i amb prou feines tenen en compte els continguts de la font de dades relacional i les característiques de l'ontologia. No obstant això, les dades poden contenir relacions ocultes que poden ajudar a la generació de mapatges. Per superar aquesta limitació, hem desenvolupat AutoMap4OBDA, un sistema que genera automàticament mapatges R2RML a partir de l'anàlisi dels continguts de la font relacional i tenint en compte les característiques de l'ontologia. El sistema fa servir una tècnica d'aprenentatge d'ontologies per inferir jerarquies de classes, selecciona les mètriques de similitud de cadenes en base a les etiquetes de les ontologies i analitza les estructures de grafs per generar els mapatges a partir de l'estructura de l'ontologia. La representació visual per mitjà d'interfícies intuïtives pot ajudar els usuaris sense coneixements tècnics a establir mapatges entre una font relacional i una ontologia. No obstant això, les eines existents per a l'edició visual de mapatges mostren algunes limitacions. En particular, la representació visual de mapatges no contempla les estructures de la font relacional i de l'ontologia de forma conjunta. Per superar aquest inconvenient, hem desenvolupat Map-On, un entorn visual web per a l'edició manual de mapatges. AutoMap4OBDA ha demostrat que supera les prestacions de les solucions existents per a la generació de mapatges. Map-On s'ha aplicat en projectes d'investigació per verificar la seva eficàcia en la gestió de mapatges.La integración de datos con formatos heterogéneos y de diversos dominios mediante tecnologías de la Web Semántica permite solventar su disparidad estructural y semántica. El acceso a datos basado en ontologías (OBDA, en inglés) es una solución integral que se basa en el uso de ontologías como esquemas mediadores y mapeos entre los datos y las ontologías para facilitar la consulta de las fuentes de datos. Sin embargo, una de las principales barreras que puede dificultar más la adopción de OBDA es la falta de herramientas para apoyar la creación de mapeos entre datos y ontologías. El objetivo de esta investigación ha sido desarrollar nuevas herramientas que permitan a expertos sin conocimientos de ontologías la creación de mapeos entre datos y ontologías. Con este fin, se han llevado a cabo dos líneas de trabajo: la generación automática de mapeos entre datos relacionales y ontologías y la edición de los mapeos a través de su representación visual. Las herramientas actualmente disponibles para automatizar la generación de mapeos están lejos de proporcionar una solución completa, ya que se basan en los esquemas relacionales y apenas tienen en cuenta los contenidos de la fuente de datos relacional y las características de la ontología. Sin embargo, los datos pueden contener relaciones ocultas que pueden ayudar a la generación de mapeos. Para superar esta limitación, hemos desarrollado AutoMap4OBDA, un sistema que genera automáticamente mapeos R2RML a partir del análisis de los contenidos de la fuente relacional y teniendo en cuenta las características de la ontología. El sistema emplea una técnica de aprendizaje de ontologías para inferir jerarquías de clases, selecciona las métricas de similitud de cadenas en base a las etiquetas de las ontologías y analiza las estructuras de grafos para generar los mapeos a partir de la estructura de la ontología. La representación visual por medio de interfaces intuitivas puede ayudar a los usuarios sin conocimientos técnicos a establecer mapeos entre una fuente relacional y una ontología. Sin embargo, las herramientas existentes para la edición visual de mapeos muestran algunas limitaciones. En particular, la representación de mapeos no contempla las estructuras de la fuente relacional y de la ontología de forma conjunta. Para superar este inconveniente, hemos desarrollado Map-On, un entorno visual web para la edición manual de mapeos. AutoMap4OBDA ha demostrado que supera las prestaciones de las soluciones existentes para la generación de mapeos. Map-On se ha aplicado en proyectos de investigación para verificar su eficacia en la gestión de mapeos.Integration of data from heterogeneous formats and domains based on Semantic Web technologies enables us to solve their structural and semantic heterogeneity. Ontology-based data access (OBDA) is a comprehensive solution which relies on the use of ontologies as mediator schemas and relational-to-ontology mappings to facilitate data source querying. However, one of the greatest obstacles in the adoption of OBDA is the lack of tools to support the creation of mappings between physically stored data and ontologies. The objective of this research has been to develop new tools that allow non-ontology experts to create relational-to-ontology mappings. For this purpose, two lines of work have been carried out: the automated generation of relational-to-ontology mappings, and visual support for mapping editing. The tools currently available to automate the generation of mappings are far from providing a complete solution, since they rely on relational schemas and barely take into account the contents of the relational data source and features of the ontology. However, the data may contain hidden relationships that can help in the process of mapping generation. To overcome this limitation, we have developed AutoMap4OBDA, a system that automatically generates R2RML mappings from the analysis of the contents of the relational source and takes into account the characteristics of ontology. The system employs an ontology learning technique to infer class hierarchies, selects the string similarity metric based on the labels of ontologies, and analyses the graph structures to generate the mappings from the structure of the ontology. The visual representation through intuitive interfaces can help non-technical users to establish mappings between a relational source and an ontology. However, existing tools for visual editing of mappings show somewhat limitations. In particular, the visual representation of mapping does not embrace the structure of the relational source and the ontology at the same time. To overcome this problem, we have developed Map-On, a visual web environment for the manual editing of mappings. AutoMap4OBDA has been shown to outperform existing solutions in the generation of mappings. Map-On has been applied in research projects to verify its effectiveness in managing mappings

Interactive graph matching and visual comparison of graphs and clustered graphs

International audienceWe introduce interactive graph matching, a process that conciliates visualization, interaction and optimization approaches to address the graph matching and graph comparison problems as a whole. Interactive graph matching is based on a multi-layered interaction model and on a visual reification of graph matching functions. We present three case studies and a system named Donatien to demonstrate the interactive graph matching approach. The three case studies involve different datasets: a) subgraphs of a lexical network, b) graph of keywords extracted from the InfoVis contest benchmark, and c) clustered graphs computed from different clustering algorithms for comparison purposes

Contributions to the cornerstones of interaction in visualization: strengthening the interaction of visualization

Visualization has become an accepted means for data exploration and analysis. Although interaction is an important component of visualization approaches, current visualization research pays less attention to interaction than to aspects of the graphical representation. Therefore, the goal of this work is to strengthen the interaction side of visualization. To this end, we establish a unified view on interaction in visualization. This unified view covers four cornerstones: the data, the tasks, the technology, and the human.Visualisierung hat sich zu einem unverzichtbaren Werkzeug für die Exploration und Analyse von Daten entwickelt. Obwohl Interaktion ein wichtiger Bestandteil solcher Werkzeuge ist, wird der Interaktion in der aktuellen Visualisierungsforschung weniger Aufmerksamkeit gewidmet als Aspekten der graphischen Repräsentation. Daher ist es das Ziel dieser Arbeit, die Interaktion im Bereich der Visualisierung zu stärken. Hierzu wird eine einheitliche Sicht auf Interaktion in der Visualisierung entwickelt

Tasks and visual techniques for the exploration of temporal graph data

This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same.The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way.While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated.Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques.The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified.The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems