Metodologías para el desarrollo de interfaces visuales de recuperación de información : análisis y comparación

Introduction. In recent years the volume of electronic information has grown exponentially. This phenomenon improves data exchange and communication but introduces new troubles in relation to information access and searching. Aim. This paper proposes an exhaustive review of the different models, methods and algorithms that can be used to develop Visual Interfaces for Information Retrieval. The methods are classified on the basis of the stage of the process in which they take part: data analysis and transformation, application of classification and visual distribution algorithms, and application of visual transformation techniques. Methodology. Based on the analysis, we compare the different methods that can be used in each stage of the production process. We also determine which combinations of methods and algorithms are most suitable at different stages. Results. In the first section, data analysis and transformation, we analyse content mining, structure mining and use mining. In the second section, visual classification algorithm, we shown the hierarchical, network, scattering and map representations. In the last section, visual transformation techniques, we present the distortion (Focus+Context) and non-distortion techniques. Conclusion. The results aim to become useful tools for other researchers when choosing a methodological combination for the development of specific proposals for visual interfaces for information retrieval, as well as suggest implications to be considered on the research of new visual transformation techniques

Explorative Graph Visualization

Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization

Metodologías para el desarrollo de interfaces visuales de recuperación de información: análisis y comparación

Introducción. Con el advenimiento de la web a principios de los años 90, el volumen de información electrónica ha experimentado un crecimiento exponencial sin precedentes. Este fenómeno introdujo muchas ventajas en relación con la posibilidad de intercambio, difusión y transferencia de datos pero, sin embargo, acarreó igualmente muchos problemas en relación con el acceso, búsqueda, localización y recuperación de la información relevante dentro de grandes volúmenes de datos. Objetivo. El presente trabajo realiza una revisión exhaustiva de los diferentes modelos, métodos y algoritmos existentes para la generación de Interfaces Visuales de Recuperación de Información (VIRIs, Visual Interfaces for Information Retrieval), clasificados según la etapa del proceso en la que intervienen: análisis y transformación de los datos, aplicación de los algoritmos de clasificación y distribución visual, y aplicación de técnicas de transformación visual. Metodología. En base a su análisis, se comparan los diferentes métodos a emplear en cada etapa del proceso de producción, así como se determinan qué combinaciones entre métodos y algoritmos de diferentes etapas resultan más adecuadas. Resultados. En la primer sección, análisis y transformación de datos, se analiza la minería de contenidos, de estructura y de uso. En la segunda sección, algoritmos de clasificación y distribución visual, se muestran las representaciones jerárquicas, de redes, de dispersión y mapas. Finalmente, entre las técnicas de transformación visual se presentan las técnicas no orientadas a la distorsión visual y las orientadas a la distorsión (Focus+Context). Conclusiones. Los resultados pretenden servir a otros investigadores como herramienta para la elección de una u otra combinación metodológica en el desarrollo de propuestas específicas de VIRIs, además de sugerir implicaciones a tener en cuenta en la necesaria investigación sobre nuevas técnicas de transformación visual.Introduction. In recent years the volume of electronic information has grown exponentially. This phenomenon improves data exchange and communication but introduces new troubles in relation to information access and searching. Aim. This paper proposes an exhaustive review of the different models, methods and algorithms that can be used to develop Visual Interfaces for Information Retrieval. The methods are classified on the basis of the stage of the process in which they take part: data analysis and transformation, application of classification and visual distribution algorithms, and application of visual transformation techniques. Methodology. Based on the analysis, we compare the different methods that can be used in each stage of the production process. We also determine which combinations of methods and algorithms are most suitable at different stages. Results. In the first section, data analysis and transformation, we analyse content mining, structure mining and use mining. In the second section, visual classification algorithm, we shown the hierarchical, network, scattering and map representations. In the last section, visual transformation techniques, we present the distortion (Focus+Context) and non-distortion techniques. Conclusion. The results aim to become useful tools for other researchers when choosing a methodological combination for the development of specific proposals for visual interfaces for information retrieval, as well as suggest implications to be considered on the research of new visual transformation techniques

Visualization of graphs and trees for software analysis

A software architecture is an abstraction of a software system, which is indispensable for many software engineering tasks. Unfortunately, in many cases information pertaining to the software architecture is not available, outdated, or inappropriate for the task at hand. The RECONSTRUCTOR project focuses on software architecture reconstruction, i.e., obtaining architectural information from an existing system. Our research, which is part of RECONSTRUCTOR, focuses on interactive visualization and tries to answer the following question: How can users be enabled to understand the large amounts of information relevant for program understanding using visual representations? To answer this question, we have iteratively developed a number of techniques for visualizing software systems. A large number of these cases consists of hierarchically organized data, combined with adjacency relations. Examples are function calls within a hierarchically organized software system and correspondence relations between two different versions of a hierarchically organized software system. Hierarchical Edge Bundles (HEBs) are used to visualize adjacency relations in hierarchically organized data, such as the aforementioned function calls within a software system. HEBs significantly reduce visual clutter by visually bundling relations together. Massive Sequence Views (MSVs) are used in conjunction with HEBs to enable analysis of sequences of relations, such as function-call traces. HEBs are furthermore used to visually compare hierarchically organized data, e.g., two different versions of a software system. HEBs visually emphasize splits, joins, and relocations of subhierarchies and provide for interactive selection of sets of relations. Since HEBs require a hierarchy to perform the bundling, we present Force-Directed Edge Bundles (FDEBs) as an alternative to visually bundle relations together in the absence of a hierarchical component. FDEBs use a self-organizing approach to bundling in which edges are modeled as flexible springs that can attract each other. As a result, visual clutter is reduced and high-level edge patterns are better visible. Finally, in all these methods, a clear depiction of the direction of edges is important. We have therefore performed a separate study in which we evaluated ten representations (including the standard arrow) for depicting directed edges in a controlled user study

Parallel Hierarchies: Interactive Visualization of Multidimensional Hierarchical Aggregates

Exploring multi-dimensional hierarchical data is a long-standing problem present in a wide range of fields such as bioinformatics, software systems, social sciences and business intelligence. While each hierarchical dimension within these data structures can be explored in isolation, critical information lies in the relationships between dimensions. Existing approaches can either simultaneously visualize multiple non-hierarchical dimensions, or only one or two hierarchical dimensions. Yet, the challenge of visualizing multi-dimensional hierarchical data remains open. To address this problem, we developed a novel data visualization approach -- Parallel Hierarchies -- that we demonstrate on a real-life SAP SE product called SAP Product Lifecycle Costing. The starting point of the research is a thorough customer-driven requirement engineering phase including an iterative design process. To avoid restricting ourselves to a domain-specific solution, we abstract the data and tasks gathered from users, and demonstrate the approach generality by applying Parallel Hierarchies to datasets from bioinformatics and social sciences. Moreover, we report on a qualitative user study conducted in an industrial scenario with 15 experts from 9 different companies. As a result of this co-innovation experience, several SAP customers requested a product feature out of our solution. Moreover, Parallel Hierarchies integration as a standard diagram type into SAP Analytics Cloud platform is in progress. This thesis further introduces different uncertainty representation methods applicable to Parallel Hierarchies and in general to flow diagrams. We also present a visual comparison taxonomy for time-series of hierarchically structured data with one or multiple dimensions. Moreover, we propose several visual solutions for comparing hierarchies employing flow diagrams. Finally, after presenting two application examples of Parallel Hierarchies on industrial datasets, we detail two validation methods to examine the effectiveness of the visualization solution. Particularly, we introduce a novel design validation table to assess the perceptual aspects of eight different visualization solutions including Parallel Hierarchies.:1 Introduction 1.1 Motivation and Problem Statement 1.2 Research Goals 1.3 Outline and Contributions 2 Foundations of Visualization 2.1 Information Visualization 2.1.1 Terms and Definition 2.1.2 What: Data Structures 2.1.3 Why: Visualization Tasks 2.1.4 How: Visualization Techniques 2.1.5 How: Interaction Techniques 2.2 Visual Perception 2.2.1 Visual Variables 2.2.2 Attributes of Preattentive and Attentive Processing 2.2.3 Gestalt Principles 2.3 Flow Diagrams 2.3.1 Classifications of Flow Diagrams 2.3.2 Main Visual Features 2.4 Summary 3 Related Work 3.1 Cross-tabulating Hierarchical Categories 3.1.1 Visualizing Categorical Aggregates of Item Sets 3.1.2 Hierarchical Visualization of Categorical Aggregates 3.1.3 Visualizing Item Sets and Their Hierarchical Properties 3.1.4 Hierarchical Visualization of Categorical Set Aggregates 3.2 Uncertainty Visualization 3.2.1 Uncertainty Taxonomies 3.2.2 Uncertainty in Flow Diagrams 3.3 Time-Series Data Visualization 3.3.1 Time & Data 3.3.2 User Tasks 3.3.3 Visual Representation 3.4 Summary ii Contents 4 Requirement Engineering Phase 4.1 Introduction 4.2 Environment 4.2.1 The Product 4.2.2 The Customers and Development Methodology 4.2.3 Lessons Learned 4.3 Visualization Requirements for Product Costing 4.3.1 Current Visualization Practice 4.3.2 Visualization Tasks 4.3.3 Data Structure and Size 4.3.4 Early Visualization Prototypes 4.3.5 Challenges and Lessons Learned 4.4 Data and Task Abstraction 4.4.1 Data Abstraction 4.4.2 Task Abstraction 4.5 Summary and Outlook 5 Parallel Hierarchies 5.1 Introduction 5.2 The Parallel Hierarchies Technique 5.2.1 The Individual Axis: Showing Hierarchical Categories 5.2.2 Two Interlinked Axes: Showing Pairwise Frequencies 5.2.3 Multiple Linked Axes: Propagating Frequencies 5.2.4 Fine-tuning Parallel Hierarchies through Reordering 5.3 Design Choices 5.4 Applying Parallel Hierarchies 5.4.1 US Census Data 5.4.2 Yeast Gene Ontology Annotations 5.5 Evaluation 5.5.1 Setup of the Evaluation 5.5.2 Procedure of the Evaluation 5.5.3 Results from the Evaluation 5.5.4 Validity of the Evaluation 5.6 Summary and Outlook 6 Visualizing Uncertainty in Flow Diagrams 6.1 Introduction 6.2 Uncertainty in Product Costing 6.2.1 Background 6.2.2 Main Causes of Bad Quality in Costing Data 6.3 Visualization Concepts 6.4 Uncertainty Visualization using Ribbons 6.4.1 Selected Visualization Techniques 6.4.2 Study Design and Procedure 6.4.3 Results 6.4.4 Discussion 6.5 Revised Visualization Approach using Ribbons 6.5.1 Application to Sankey Diagram 6.5.2 Application to Parallel Sets 6.5.3 Application to Parallel Hierarchies 6.6 Uncertainty Visualization using Nodes 6.6.1 Visual Design of Nodes 6.6.2 Expert Evaluation 6.7 Summary and Outlook 7 Visual Comparison Task 7.1 Introduction 7.2 Comparing Two One-dimensional Time Steps 7.2.1 Problem Statement 7.2.2 Visualization Design 7.3 Comparing Two N-dimensional Time Steps 7.4 Comparing Several One-dimensional Time Steps 7.5 Summary and Outlook 8 Parallel Hierarchies in Practice 8.1 Application to Plausibility Check Task 8.1.1 Plausibility Check Process 8.1.2 Visual Exploration of Machine Learning Results 8.2 Integration into SAP Analytics Cloud 8.2.1 SAP Analytics Cloud 8.2.2 Ocean to Table Project 8.3 Summary and Outlook 9 Validation 9.1 Introduction 9.2 Nested Model Validation Approach 9.3 Perceptual Validation of Visualization Techniques 9.3.1 Design Validation Table 9.3.2 Discussion 9.4 Summary and Outlook 10 Conclusion and Outlook 10.1 Summary of Findings 10.2 Discussion 10.3 Outlook A Questionnaires of the Evaluation B Survey of the Quality of Product Costing Data C Questionnaire of Current Practice Bibliograph