Quantitative Comparison of Dynamic Treemaps for Software Evolution Visualization

Dynamic treemaps are one of the methods of choice for displaying large hierarchies that change over time, such as those encoding the structure of evolving software systems. While quality criteria (and algorithms that optimize for them) are known for static trees, far less has been studied for treemapping dynamic trees. We address this gap by proposing a methodology and associated quality metrics to measure the quality of dynamic treemaps for the specific use-case and context of software evolution visualization. We apply our methodology on a benchmark containing a wide range of real-world software repositories and 12 well-known treemap algorithms. Based on our findings, we discuss the observed advantages and limitations of various treemapping algorithms for visualizing software structure evolution, and propose ways for users to choose the most suitable treemap algorithm based on the targeted criteria of interest

A Stable Greedy Insertion Treemap Algorithm for Software Evolution Visualization

Computing treemap layouts for time-dependent (dynamic) trees is an open problem in information visualization. In particular, the constraints of spatial quality (cell aspect ratio) and stability (small treemap changes mandated by given tree-data changes) are hard to satisfy simultaneously. Most existing treemap methods focus on spatial quality, but are not inherently designed to address stability. We propose here a new treemapping method that aims to jointly optimize both these constraints. Our method is simple to implement, generic (handles any types of dynamic hierarchies), and fast. We compare our method with 14 state of the art treemaping algorithms using four quality metrics, over 28 dynamic hierarchies extracted from evolving software codebases. The comparison shows that our proposal jointly optimizes spatial quality and stability better than existing methods

Space Partitioning Schemes and Algorithms for Generating Regular and Spiral Treemaps

Treemaps have been widely applied to the visualization of hierarchical data. A treemap takes a weighted tree and visualizes its leaves in a nested planar geometric shape, with sub-regions partitioned such that each sub-region has an area proportional to the weight of its associated leaf nodes. Efficiently generating visually appealing treemaps that also satisfy other quality criteria is an interesting problem that has been tackled from many directions. We present an optimization model and five new algorithms for this problem, including two divide and conquer approaches and three spiral treemap algorithms. Our optimization model is able to generate superior treemaps that could serve as a benchmark for comparing the quality of more computationally efficient algorithms. Our divide and conquer and spiral algorithms either improve the performance of their existing counterparts with respect to aspect ratio and stability or perform competitively. Our spiral algorithms also expand their applicability to a wider range of input scenarios. Four of these algorithms are computationally efficient as well with quasilinear running times and the last algorithm achieves a cubic running time. A full version of this paper with all appendices, data, and source codes is available at \anonymizeOSF{\OSFSupplementText}

Visualization of dynamic multidimensional and hierarchical datasets

When it comes to tools and techniques designed to help understanding complex abstract data, visualization methods play a prominent role. They enable human operators to lever age their pattern finding, outlier detection, and questioning abilities to visually reason about a given dataset. Many methods exist that create suitable and useful visual represen tations of static abstract, non-spatial, data. However, for temporal abstract, non-spatial, datasets, in which the data changes and evolves through time, far fewer visualization tech niques exist. This thesis focuses on the particular cases of temporal hierarchical data representation via dynamic treemaps, and temporal high-dimensional data visualization via dynamic projec tions. We tackle the joint question of how to extend projections and treemaps to stably, accurately, and scalably handle temporal multivariate and hierarchical data. The literature for static visualization techniques is rich and the state-of-the-art methods have proven to be valuable tools in data analysis. Their temporal/dynamic counterparts, however, are not as well studied, and, until recently, there were few hierarchical and high-dimensional methods that explicitly took into consideration the temporal aspect of the data. In addi tion, there are few or no metrics to assess the quality of these temporal mappings, and even fewer comprehensive benchmarks to compare these methods. This thesis addresses the abovementioned shortcomings. For both dynamic treemaps and dynamic projections, we propose ways to accurately measure temporal stability; we eval uate existing methods considering the tradeoff between stability and visual quality; and we propose new methods that strike a better balance between stability and visual quality than existing state-of-the-art techniques. We demonstrate our methods with a wide range of real-world data, including an application of our new dynamic projection methods to support the analysis and classification of hyperkinetic movement disorder data.Quando se trata de ferramentas e técnicas projetadas para ajudar na compreensão dados abstratos complexos, métodos de visualização desempenham um papel proeminente. Eles permitem que os operadores humanos alavanquem suas habilidades de descoberta de padrões, detecção de valores discrepantes, e questionamento visual para a raciocinar sobre um determinado conjunto de dados. Existem muitos métodos que criam representações visuais adequadas e úteis de para dados estáticos, abstratos, e não-espaciais. No entanto, para dados temporais, abstratos, e não-espaciais, isto é, dados que mudam e evoluem no tempo, existem poucas técnicas apropriadas. Esta tese concentra-se nos casos específicos de representação temporal de dados hierárquicos por meio de treemaps dinâmicos, e visualização temporal de dados de alta dimen sionalidade via projeções dinâmicas. Nós abordar a questão conjunta de como estender projeções e treemaps de forma estável, precisa e escalável para lidar com conjuntos de dados hierárquico-temporais e multivariado-temporais. Em ambos os casos, a literatura para técnicas estáticas é rica e os métodos estado da arte provam ser ferramentas valiosas em análise de dados. Suas contrapartes temporais/dinâmicas, no entanto, não são tão bem estudadas e, até recentemente, existiam poucos métodos hierárquicos e de alta dimensão que explicitamente levavam em consideração o aspecto temporal dos dados. Além disso, existiam poucas métricas para avaliar a qualidade desses mapeamentos visuais temporais, e ainda menos benchmarks abrangentes para comparação esses métodos. Esta tese aborda as deficiências acima mencionadas para treemaps dinâmicos e projeções dinâmicas. Propomos maneiras de medir com precisão a estabilidade temporal; avalia mos os métodos existentes, considerando o compromisso entre estabilidade e qualidade visual; e propomos novos métodos que atingem um melhor equilíbrio entre estabilidade e a qualidade visual do que as técnicas estado da arte atuais. Demonstramos nossos mé todos com uma ampla gama de dados do mundo real, incluindo uma aplicação de nossos novos métodos de projeção dinâmica para apoiar a análise e classificação dos dados de transtorno de movimentos

Fat Polygonal Partitions with Applications to Visualization and Embeddings

Let $\mathcal{T}$ be a rooted and weighted tree, where the weight of any node is equal to the sum of the weights of its children. The popular Treemap algorithm visualizes such a tree as a hierarchical partition of a square into rectangles, where the area of the rectangle corresponding to any node in $\mathcal{T}$ is equal to the weight of that node. The aspect ratio of the rectangles in such a rectangular partition necessarily depends on the weights and can become arbitrarily high. We introduce a new hierarchical partition scheme, called a polygonal partition, which uses convex polygons rather than just rectangles. We present two methods for constructing polygonal partitions, both having guarantees on the worst-case aspect ratio of the constructed polygons; in particular, both methods guarantee a bound on the aspect ratio that is independent of the weights of the nodes. We also consider rectangular partitions with slack, where the areas of the rectangles may differ slightly from the weights of the corresponding nodes. We show that this makes it possible to obtain partitions with constant aspect ratio. This result generalizes to hyper-rectangular partitions in $\mathbb{R}^d$. We use these partitions with slack for embedding ultrametrics into $d$-dimensional Euclidean space: we give a $\mathop{\rm polylog}(\Delta)$-approximation algorithm for embedding $n$-point ultrametrics into $\mathbb{R}^d$ with minimum distortion, where $\Delta$ denotes the spread of the metric, i.e., the ratio between the largest and the smallest distance between two points. The previously best-known approximation ratio for this problem was polynomial in $n$. This is the first algorithm for embedding a non-trivial family of weighted-graph metrics into a space of constant dimension that achieves polylogarithmic approximation ratio.Comment: 26 page

Visualization of dynamic multidimensional and hierarchical datasets

When it comes to tools and techniques designed to help understanding complex abstract data, visualization methods play a prominent role. They enable human operators to lever age their pattern finding, outlier detection, and questioning abilities to visually reason about a given dataset. Many methods exist that create suitable and useful visual represen tations of static abstract, non-spatial, data. However, for temporal abstract, non-spatial, datasets, in which the data changes and evolves through time, far fewer visualization tech niques exist. This thesis focuses on the particular cases of temporal hierarchical data representation via dynamic treemaps, and temporal high-dimensional data visualization via dynamic projec tions. We tackle the joint question of how to extend projections and treemaps to stably, accurately, and scalably handle temporal multivariate and hierarchical data. The literature for static visualization techniques is rich and the state-of-the-art methods have proven to be valuable tools in data analysis. Their temporal/dynamic counterparts, however, are not as well studied, and, until recently, there were few hierarchical and high-dimensional methods that explicitly took into consideration the temporal aspect of the data. In addi tion, there are few or no metrics to assess the quality of these temporal mappings, and even fewer comprehensive benchmarks to compare these methods. This thesis addresses the abovementioned shortcomings. For both dynamic treemaps and dynamic projections, we propose ways to accurately measure temporal stability; we eval uate existing methods considering the tradeoff between stability and visual quality; and we propose new methods that strike a better balance between stability and visual quality than existing state-of-the-art techniques. We demonstrate our methods with a wide range of real-world data, including an application of our new dynamic projection methods to support the analysis and classification of hyperkinetic movement disorder data.Quando se trata de ferramentas e técnicas projetadas para ajudar na compreensão dados abstratos complexos, métodos de visualização desempenham um papel proeminente. Eles permitem que os operadores humanos alavanquem suas habilidades de descoberta de padrões, detecção de valores discrepantes, e questionamento visual para a raciocinar sobre um determinado conjunto de dados. Existem muitos métodos que criam representações visuais adequadas e úteis de para dados estáticos, abstratos, e não-espaciais. No entanto, para dados temporais, abstratos, e não-espaciais, isto é, dados que mudam e evoluem no tempo, existem poucas técnicas apropriadas. Esta tese concentra-se nos casos específicos de representação temporal de dados hierárquicos por meio de treemaps dinâmicos, e visualização temporal de dados de alta dimen sionalidade via projeções dinâmicas. Nós abordar a questão conjunta de como estender projeções e treemaps de forma estável, precisa e escalável para lidar com conjuntos de dados hierárquico-temporais e multivariado-temporais. Em ambos os casos, a literatura para técnicas estáticas é rica e os métodos estado da arte provam ser ferramentas valiosas em análise de dados. Suas contrapartes temporais/dinâmicas, no entanto, não são tão bem estudadas e, até recentemente, existiam poucos métodos hierárquicos e de alta dimensão que explicitamente levavam em consideração o aspecto temporal dos dados. Além disso, existiam poucas métricas para avaliar a qualidade desses mapeamentos visuais temporais, e ainda menos benchmarks abrangentes para comparação esses métodos. Esta tese aborda as deficiências acima mencionadas para treemaps dinâmicos e projeções dinâmicas. Propomos maneiras de medir com precisão a estabilidade temporal; avalia mos os métodos existentes, considerando o compromisso entre estabilidade e qualidade visual; e propomos novos métodos que atingem um melhor equilíbrio entre estabilidade e a qualidade visual do que as técnicas estado da arte atuais. Demonstramos nossos mé todos com uma ampla gama de dados do mundo real, incluindo uma aplicação de nossos novos métodos de projeção dinâmica para apoiar a análise e classificação dos dados de transtorno de movimentos

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

Exploring Hierarchical Visualization Designs Using Phylogenetic Trees

Ongoing research on information visualization has produced an ever-increasing number of visualization designs. Despite this activity, limited progress has been made in categorizing this large number of information visualizations. This makes understanding their common design features challenging, and obscures the yet unexplored areas of novel designs. With this work, we provide categorization from an evolutionary perspective, leveraging a computational model to represent evolutionary processes, the phylogenetic tree. The result — a phylogenetic tree of a design corpus of hierarchical visualizations — enables better understanding of the various design features of hierarchical information visualizations, and further illuminates the space in which the visualizations lie, through support for interactive clustering and novel design suggestions. We demonstrate these benefits with our software system, where a corpus of two-dimensional hierarchical visualization designs is constructed into a phylogenetic tree. This software system supports visual interactive clustering and suggesting for novel designs; the latter capacity is also demonstrated via collaboration with an artist who sketched new designs using our system

A usability study on the use of multi-context visualization

Graph visualization has been widely used in real-world applications, as it provides better presentation of overall data structure. However, there are navigation problems existing in deep and large relational datasets. To address these challenges, a new technique called multi-context visualization, which provides users with rich contextual information, has been proposed as the solution to the navigation in large scale datasets. This paper evaluates the multi-context visualization by conducting an experiment-based user study. To answer whether the more contextual information positively assist in making more accurate and easier decisions, it aims to evaluate the effectiveness and efficiency of the multi-context visualization, by measuring the user performance. Specifically, this usability test was designed to test if the use of multiple context views can improve navigation problems for deep and large relational data sets. © 2008 IEEE