Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (catneuro) To The Deep Learning Of Game Controller

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises about whether KD mechanisms that also incorporate cooperation can perform better in such environments than purely competitive ones? Borrowing from game theory, game-based KD mechanisms are implemented and tested against the default competitive mechanism – Weighted Majority (WTD). Two different concepts of complexity are addressed – numerical optimization under dynamic environments and hierarchal, multi-objective optimization for evolving deep learning models. The former is addressed with the CATGame software system and the later with CATNeuro. CATGame implements three types of games that span both cooperation and competition for knowledge distribution, namely: Iterated Prisoner\u27s Dilemma (IPD), Stag-Hunt and Stackelberg. The performance of the three game mechanisms is compared with the aid of a dynamic problem generator called Cones World. Weighted Majority, aka “wisdom of the crowd”, the default CA competitive KD mechanism is used as the benchmark. It is shown that games that support both cooperation and competition do indeed perform better but not in all cases. The results shed light on what kinds of games are suited to problem solving in complex, dynamic environments. Specifically, games that balance exploration and exploitation using the local signal of ‘social’ rank – Stag-Hunt and IPD – perform better. Stag-Hunt which is also the most cooperative of the games tested, performed the best overall. Dynamic analysis of the ‘social’ aspects of the CA test runs shows that Stag-Hunt allocates compute resources more consistently than the others in response to environmental complexity changes. Stackelberg where the allocation decisions are centralized, like in a centrally planned economic system, is found to be the least adaptive. CATNeuro is for solving neural architecture search (NAS) problems. Contemporary ‘deep learning’ neural network models are proven effective. However, the network topologies may be complex and not immediately obvious for the problem at hand. This has given rise to the secondary field of neural architecture search. It is still nascent with many frameworks and approaches now becoming available. This paper describes a NAS method based on graph evolution pioneered by NEAT (Neuroevolution of Augmenting Topologies) but driven by the evolutionary mechanisms under Cultural Algorithms. Here CATNeuro is applied to find optimal network topologies to play a 2D fighting game called FightingICE (derived from “The Rumble Fish” video game). A policy-based, reinforcement learning method is used to create the training data for network optimization. CATNeuro is still evolving. To inform the development of CATNeuro, in this primary foray into NAS, we contrast the performance of CATNeuro with two different knowledge distribution mechanisms – the stalwart Weighted Majority and a new one based on the Stag-Hunt game from evolutionary game theory that performed the best in CATGame. The research shows that Stag-Hunt has a distinct edge over WTD in terms of game performance, model accuracy, and model size. It is therefore deemed to be the preferred mechanism for complex, hierarchical optimization tasks such as NAS and is planned to be used as the default KD mechanism in CATNeuro going forward

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

Visualising Topological Structures of Activation in Artificial Neural Networks

This project report describes a first approach to creating a visualisation of an artificial neural network, that visualises the topology of the network given an individual data input that the network has learned to recognise. A survey of previous attempts to visualise both artificial and biological neural networks is presented, as well as a survey of various techniques used in other forms of network visualisation that could be applied to visualising artificial neural networks. This is followed by a detailed description of the method implemented in this project, followed by results from the visualisatio

An Investigation into Visual Graph Comparison

Information Visualisation is extensively used in single graph analysis. However, relatively little work has been done in the field of graph comparison. This work examines and compares the use of two standard graph representations in this area, the Node-Link representation and one based on the graph adjacency matrix. It considers which representation method is superior. In addition it explores whether it is best, for comparison purposes, to combine multiple graphs into single views or to juxtapose single graph representations.To run this comparison a simple tool was developed and task-based analysis done using that tool to compare multiple versions of a small, locally dense, directed multigraph based on sports data. We are able to demonstrate that it is better to combine views into a single diagram, and that even for small graphs, an analyst is not disadvantaged by the abstract nature of the matrix compared to the intuitive Node-Link diagram

How to tell stories using visualization: strategies towards narrative visualization

Os benefícios da utilização das narrativas são desde há muito conhecidos e o seu potencial para simplificar conceitos, transmitir valores culturais e experiências, criar ligações emocionais e capacidade para ajudar a reter a informação tem sido explorado em diferentes áreas. As narrativas não são só a principal forma como as pessoas obtêm o sentido do mundo, mas também a forma mais fácil que encontrámos para partilhar informações complexas. Devido ao seu potencial, as narrativas foram recentemente abordadas na área da Visualização de Informação e do Conhecimento, muitas vezes apelidada de Visualização Narrativa. Esta questão é particularmente importante para os media, uma das áreas que tem impulsionado a investigação em Visualização Narrativa. A necessidade de incorporar histórias nas visualizações surge da necessidade de partilhar dados complexos de um modo envolvente. Hoje em dia somos confrontados com a elevada quantidade de informação disponível, um desafio difícil de resolver. Os avanços da tecnologia permitiram ir além das formas tradicionais de narrativa e de representação de dados, dando-nos meios mais atraentes e sofisticados para contar histórias. Nesta tese, exploro os benefícios da introdução de narrativas nas visualizações. Adicionalmente também exploro formas de combinar histórias com a visualizações e métodos eficientes para representar e dar sentido aos dados de uma forma que permite que as pessoas se relacionem com a informação. Esta investigação está bastante próxima da área do jornalismo, no entanto estas técnicas podem ser aplicadas em diferente áreas (educação, visualização científica, etc.). Para explorar ainda mais este tema foi adotada um avaliação que utiliza diferentes metodologias como a tipologia, vários casos de estudo, um estudo com grupos de foco, e ainda estudos de design e análise de técnicas.The benefits of storytelling are long-known and its potential to simplify concepts, convey cultural values and experiences, create emotional connection, and capacity to help retain information has been explored in di erent areas, such as journalism, education, marketing, and others. Narratives not only have been the main way people make sense of the world, but also the easiest way humans found out to share complex information. Due to its potential narratives have also recently been approached in the area of Information and Knowledge Visualization, several times being referred to as Narrative Visualization. This matter is also particularly important for news media, one of the areas that has been pushing the research on Narrative Visualization. The necessity to incorporate storytelling in visualizations arises from the need to share complex data in a way that is engaging. Nowadays we also have the challenge of the high amount of information available, which can be hard to cope with. Advances in technology have enabled us to go beyond the traditional forms of storytelling and representing data, giving us more attractive and sophisticated means to tell stories. In this dissertation, I explore the benefits of infusing visualizations with narratives. In addition I also present ways of combining storytelling with visualization and e cient methods to represent and make sense of data in a way that allows people to relate with the information. This research is closely related to journalism, but these techniques can be applied to completely di erent areas (education, scientific visualization, etc.). To further explore this topic a mixedmethod evaluation that consists of a typology, several case studies and a focus group study was chosen, as well as design studies and techniques review. This dissertation is intended to contribute to the evolving understanding of the field of narrative visualization

Network Analysis and Modeling in Systems Biology

This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this thesis? They range from the simplest biological agents, the viruses, in this case the Potyvirus genus to prokariotes such as Escherichia coli and complex eukariotes (Arabidopsis thaliana, Nicotiana benthamiana). The second axis refers to which biological networks were studied. Those are protein-protein interaction (PPIN) and metabolic networks (MN). The final axis relates to the mathematical and modelling tools used to generate knowledge from those networks. These tools can be classify in three main branches: graph theory, constraint-based modelling and multivariate statistics. The document is structured in six parts. The first part states the justification for the thesis, exposes a general thesis roadmap and enumerates its main contributions. In the second part important literature is reviewed, summarized and integrated. From the birth and development of Systems Biology to one of its most popular branches: biological network analysis. Particular focus is put on PPIN and MN and their structure, representations and features. Finally a general overview of the mathematical tools used is presented. The third, fourth and fifth parts represent the central work of this thesis. They deal respectively with genotypephenotype interaction and classical network analysis, constraint-based modelling methods comparison and modelling metabolic networks and community structure. Finally, in the sixth part the main conclusions of the thesis are summarized and enumerated. This thesis highlights the vital importance of studying biological entities as systems and how powerful and promising this integrated analysis is. Particularly, network analysis becomes a fundamental avenue of research to gain insight into those biological systems and to extract, integrate and display this new information. It generates knowledge from just data.Esta tesis está dedicada al estudio y comprensión de redes biológicas a nivel molecular. Los objetivos fueron analizar su topología, integrar esta en un análisis de genotipo-fenotipo, desarrollar descripciones matemáticas más completas para ellas, estudiar su estructura de comunidades y comparar diferentes metodologías para estimar sus flujos internos. El trabajo presentado en este documento gira entorno a tres ejes principales. El primero es el biológico. ¿Qué organismos han sido estudiados en esta tesis? Estos van desde los agentes biológicos mas simples, los virus, en este caso el género Potyvirus, hasta procariotas como Escherichia coli y eucariotas complejos (Arabidopsis thaliana, Nicotiana benthamiana). El segundo eje hace referencia a las redes biológicas estudiadas, que fueron redes de interacción de proteínas (PPIN) y redes metabólicas (MN). El eje final es el de las herramientas matemáticas y de modelización empleadas para interrogar esas redes. Estas herramientas pueden clasificarse en tres grandes grupos: teoría de grafos, modelización basada en restricciones y estadística multivariante. Este documento está estructurado en seis partes. La primera expone la justificación para la tesis, muestra un mapa visual de la misma y enumera sus contribuciones principales. En la segunda parte, la bibliografía relevante es revisada y resumida. Desde el nacimiento y desarrollo de la Biología de Sistemas hasta una de sus ramas más populares: el análisis de redes biomoleculares. Especial interés es puesto en PPIN y MN: su estructura, representación y características. Finalmente, un resumen general de las herramientas matemáticas usadas es presentado. Los capítulos tercero, cuarto y quinto representan el cuerpo central de esta tesis. Estos tratan respectivamente sobre la interacción de genotipo-fenotipo y análisis topolólogico clásico de redes, modelos basados en restricciones y modelización de redes metabólicas y su estructura de comunidades. Finalmente, en la sexta parte las principales conclusiones de la tesis son resumidas y expuestas. Esta tesis pone énfasis en la vital importancia de estudiar los fenómenos biológicos como sistemas y en la potencia y prometedor futuro de este análisis integrativo. En concreto el análisis de redes supone un camino de investigación fundamental para obtener conocimiento sobre estos sistemas biológicos y para extraer y mostrar información sobre los mismos. Este análisis genera conocimiento partiendo únicamente desde datos.Aquesta tesi està dedicada a l'estudi i comprensió de xarxes biològiques a nivell molecular. Els objectius van ser analitzar la seva topologia, integrar aquesta en una anàlisi de genotip-fenotip, desenvolupar descripcions matemàtiques més completes per a elles, estudiar la seva estructura de comunitats o modularitat i comparar diferents metodologies per estimar els fluxos interns. El treball presentat en aquest document gira entorn de tres eixos principals. El primer és el biològic. ¿Què organismes han estat estudiats en aquesta tesi? Aquests van des dels agents biològics mes simples, els virus, en aquest cas el gènere Potyvirus, fins procariotes com Escherichia coli i eucariotes complexos (Arabidopsis thaliana, Nicotiana benthamiana). El segon eix fa referència a les xarxes biològiques estudiades, que van ser les xarxes d'interacció de proteïnes (PPIN) i les xarxes metabòliques (MN). L'eix final és el de les eines matemàtiques i de modelització emprades per interrogar aquestes xarxes. Aquestes eines poden classificarse en tres grans grups: teoria de grafs, modelització basada en restriccions i estadística multivariant. Aquest document està estructurat en sis parts. La primera exposa la justificació per a la tesi, mostra un mapa visual de la mateixa i enumera les seves contribucions principals. A la segona part, la bibliografia rellevant és revisada i resumida. Des del naixement i desenvolupament de la Biologia de Sistemes fins a una de les seves branques més populars: l'anàlisi de xarxes moleculars. Especial interès és posat en PPIN i MN: la seva estructura, representació i característiques. Finalment, un resum general de les eines matemàtiques utilitzades és presentat. Els capítols tercer, quart i cinquè representen el cos central d'aquesta tesi. Aquests tracten respectivament sobre la interacció de genotip-fenotip i anàlisi topolólogico clàssic de xarxes, models basats en restriccions i modelització de xarxes metabòliques i la seva estructura de comunitats. Finalment, en la sisena part les principals conclusions de la tesi són resumides i exposades. Aquesta tesi posa èmfasi en la vital importància d'estudiar els fenòmens biològics com sistemes i en la potència i prometedor futur d'aquesta anàlisi integratiu. En concret l'anàlisi de xarxes suposa un camí d'investigació fonamental per obtenir coneixement sobre aquests sistemes biològics i per extreure i mostrar informació sobre els mateixos. Aquest anàlisi genera coneixement partint únicament des de dades.Bosque Chacón, G. (2017). Network Analysis and Modeling in Systems Biology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/79082TESI

Close and Distant Reading Visualizations for the Comparative Analysis of Digital Humanities Data

Traditionally, humanities scholars carrying out research on a specific or on multiple literary work(s) are interested in the analysis of related texts or text passages. But the digital age has opened possibilities for scholars to enhance their traditional workflows. Enabled by digitization projects, humanities scholars can nowadays reach a large number of digitized texts through web portals such as Google Books or Internet Archive. Digital editions exist also for ancient texts; notable examples are PHI Latin Texts and the Perseus Digital Library. This shift from reading a single book “on paper” to the possibility of browsing many digital texts is one of the origins and principal pillars of the digital humanities domain, which helps developing solutions to handle vast amounts of cultural heritage data – text being the main data type. In contrast to the traditional methods, the digital humanities allow to pose new research questions on cultural heritage datasets. Some of these questions can be answered with existent algorithms and tools provided by the computer science domain, but for other humanities questions scholars need to formulate new methods in collaboration with computer scientists. Developed in the late 1980s, the digital humanities primarily focused on designing standards to represent cultural heritage data such as the Text Encoding Initiative (TEI) for texts, and to aggregate, digitize and deliver data. In the last years, visualization techniques have gained more and more importance when it comes to analyzing data. For example, Saito introduced her 2010 digital humanities conference paper with: “In recent years, people have tended to be overwhelmed by a vast amount of information in various contexts. Therefore, arguments about ’Information Visualization’ as a method to make information easy to comprehend are more than understandable.” A major impulse for this trend was given by Franco Moretti. In 2005, he published the book “Graphs, Maps, Trees”, in which he proposes so-called distant reading approaches for textual data that steer the traditional way of approaching literature towards a completely new direction. Instead of reading texts in the traditional way – so-called close reading –, he invites to count, to graph and to map them. In other words, to visualize them. This dissertation presents novel close and distant reading visualization techniques for hitherto unsolved problems. Appropriate visualization techniques have been applied to support basic tasks, e.g., visualizing geospatial metadata to analyze the geographical distribution of cultural heritage data items or using tag clouds to illustrate textual statistics of a historical corpus. In contrast, this dissertation focuses on developing information visualization and visual analytics methods that support investigating research questions that require the comparative analysis of various digital humanities datasets. We first take a look at the state-of-the-art of existing close and distant reading visualizations that have been developed to support humanities scholars working with literary texts. We thereby provide a taxonomy of visualization methods applied to show various aspects of the underlying digital humanities data. We point out open challenges and we present our visualizations designed to support humanities scholars in comparatively analyzing historical datasets. In short, we present (1) GeoTemCo for the comparative visualization of geospatial-temporal data, (2) the two tag cloud designs TagPies and TagSpheres that comparatively visualize faceted textual summaries, (3) TextReuseGrid and TextReuseBrowser to explore re-used text passages among the texts of a corpus, (4) TRAViz for the visualization of textual variation between multiple text editions, and (5) the visual analytics system MusikerProfiling to detect similar musicians to a given musician of interest. Finally, we summarize our and the collaboration experiences of other visualization researchers to emphasize the ingredients required for a successful project in the digital humanities, and we take a look at future challenges in that research field

Visualização de fluxos migratórios : os candidatos ao ensino superior público (2012 a 2014)

Mestrado em Sistemas de InformaçãoA importância e quantidade da informação criada pelos vários sectores da sociedade, tornam a visualização e compreensão numa das tarefas mais importantes de hoje em dia. A utilização da Visualização de Informação para representar diferentes tipos de dados de uma forma mais compreensível tem-se generalizado em várias áreas. Esta dissertação tem como objectivo investigar como é que a Visualização de Informação poderá ser usada para aumentar a legibilidade e usabilidade de dados de migração dos candidatos ao Ensino Superior português. Atualmente existem um conjunto diverso de visualizações para ilustrar este tipo de informação. Este trabalho pretende desenvolver visualizações eficientes e interativas para analisar as migrações dos candidatos ao Ensino Superior, utilizando apenas ferramentas Web e validar se os utilizador conseguem compreender a informação apresentada. Esta dissertação apresenta uma breve introdução à Visualização de Informação, sintetiza as representações de fluxos migratórios mais relevantes até ao momento, apresenta algumas ferramentas e bibliotecas para a sua implementação em ambiente Web. As três representações criadas são apresentadas e validadas através de avaliação heurísticas e testes de usabilidade, exemplificando assim todo o ciclo de desenvolvimento necessário para criar uma conjunto de visualizações utilizando um caso de estudo real.The use of Information Visualization methods to represent different types of data in a more understandable way has become widespread in various fields. This thesis aims to investigate how Information Visualization can be used to increase the readability and usability of the annual data associated with the placement of Portuguese Higher Education candidates. Currently there are multiple visualizations available to illustrate this type of information. The aim is to develop efficient and interactive visualizations to analyze the candidates’ placement/migration, using only Web tools, and to validate that the user can effectively use the visualizations and understand the information presented. This work presents a brief introduction to Information Visualization, summarizes the most important representations for migratory flows, and presents some tools and libraries for an implementation in a Web environment. The three representations developed are presented and validated, through heuristic evaluation and usability testing, thus illustrating the entire development cycle for creating a set of visualizations within a real case study scenario