Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables to discover similar temporal summaries (e.g., recurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.Comment: IEEE Transactions on Visualization and Computer Graphics (TVCG), to appea

Persistent Homology Guided Force-Directed Graph Layouts

Graphs are commonly used to encode relationships among entities, yet their abstractness makes them difficult to analyze. Node-link diagrams are popular for drawing graphs, and force-directed layouts provide a flexible method for node arrangements that use local relationships in an attempt to reveal the global shape of the graph. However, clutter and overlap of unrelated structures can lead to confusing graph visualizations. This paper leverages the persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout. In particular, the user adds and removes contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Finally, we demonstrate the utility of our approach across a variety of synthetic and real datasets

How Evolutionary Visual Software Analytics Supports Knowledge Discovery

[EN] Evolutionary visual software analytics is a specialization of visual analytics. It is aimed at supporting software maintenance processes by aiding the understanding and comprehension of software evolution with the active participation of users. Therefore, it deals with the analysis of software projects that have been under development and maintenance for several years and which are usually formed by thousands of software artifacts,which are also associated to logs from communications, defect-tracking and software configuration management systems. Accordingly, evolutionary visual software analytics aims to assist software developers and software project managers by means of an integral approach that takes into account knowledge extraction techniques as well as visual representations that make use of interaction techniques and linked views. Consequently,this paper discusses the implementation of an architecture based on the evolutionary visual software analytics process and how it supports knowledge discovery during software maintenance tasks.[ES] Analítica de software visual evolutivos es una especialización de la analítica visual. Está dirigido a apoyar los procesos de mantenimiento de software, ayudando al entendimiento y la comprensión de la evolución del software, con la participación activa de los usuarios. Por lo tanto, tiene que ver con el análisis de los proyectos de software que han estado bajo desarrollo y mantenimiento por varios años y que por lo general están formados por miles de artefactos de software, que también están asociadas a los registros de las comunicaciones, seguimiento de defectos y sistemas de gestión de configuración de software. En consecuencia, la analítica de software visual evolutivos tiene como objetivo ayudar a los desarrolladores de software y administradores de proyectos de software a través de un enfoque integral que tenga en cuenta las técnicas de extracción de conocimiento, así como representaciones visuales que hacen uso de técnicas de interacción y vistas enlazadas. En consecuencia, en este documento se analiza la implementación de una arquitectura basada en el proceso de analítica de software visual evolutivos y la forma en que apoya el descubrimiento de conocimiento durante las tareas de mantenimiento de softwar

Visualization and Evolution of Software Architectures

Software systems are an integral component of our everyday life as we find them in tools and embedded in equipment all around us. In order to ensure smooth, predictable, and accurate operation of these systems, it is crucial to produce and maintain systems that are highly reliable. A well-designed and well-maintained architecture goes a long way in achieving this goal. However, due to the intangible and often complex nature of software architecture, this task can be quite complicated. The field of software architecture visualization aims to ease this task by providing tools and techniques to examine the hierarchy, relationship, evolution, and quality of architecture components. In this paper, we present a discourse on the state of the art of software architecture visualization techniques. Further, we highlight the importance of developing solutions tailored to meet the needs and requirements of the stakeholders involved in the analysis process

The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy