Dissertations / Theses on the topic 'Graph-Based visualization and structuring'

Cette thèse explore des approches de structuration automatique et semi-automatique pour les collections de contenus iconographiques patrimoniaux. La structuration et l'exploitation de tels contenus pourrait s'avérer bénéfique pour de nombreuses applications, du tourisme virtuel à un accès facilité pour les chercheurs et le grand public. Cependant, l'organisation "en silo" inhérente à ces collections entrave les approches de structuration automatique et toutes les applications subséquentes.La communauté de la vision par ordinateur a proposé de nombreuses méthodes automatiques pour l'indexation (et la structuration) de collections d'images à grande échelle. Exploitant l'aspect visuel des contenus, elles fonctionnent indépendamment des structures de métadonnées qui organisent principalement les collections patrimoniales, apparaissant ainsi comme une solution potentielle au problème de liage entre les structures uniques des différentes collections. Cependant, ces méthodes sont généralement entrainées sur de grands jeux d'images récentes ne reflétant pas la diversité visuelle des contenus patrimoniaux. Cette thèse vise à évaluer et à améliorer ces méthodes automatiques pour la structuration des contenus iconographiques patrimoniaux. Pour cela, cette thèse apporte trois différentes contributions avec l'objectif commun d'assurer une certaine explicabilité des méthodes évaluées et proposées, nécessaire pour justifier de leur pertinence et faciliter leur adaptation à de nouvelles acquisitions. La première contribution est une évaluation des approches automatiques de recherche d'images basée sur le contenu, confrontées aux différents types de données du patrimoine iconographique. Cette évaluation se concentre d'abord sur les descripteurs d'images de l'étape de recherche d'images, puis sur les méthodes de ré-ordonnancement qui réorganisent ensuite les images similaires en fonction d'un autre critère. Les approches les plus pertinentes peuvent alors être sélectionnées pour la suite tandis que celles qui ne le sont pas fournissent des informations inspirant notre deuxième contribution. La deuxième contribution consiste en trois nouvelles méthodes de ré-ordonnancement exploitant des informations spatiales plus ou moins globales pour réévaluer les liens de similarité visuelle créés par l'étape de recherche d'images. La première exploite les premières images retrouvées pour créer une scène 3D approximative dans laquelle les images retrouvées sont positionnées pour évaluer leur cohérence dans la scène. La deuxième simplifie la première avec une expansion de requête géométrique, c'est-à-dire en agrégeant des informations géométriques 2D issues des images récupérées pour encoder plus largement la géométrie de la scène sans la reconstruire (ce qui est couteux en temps de calcul). Enfin, la troisième exploite des informations de position plus globales, à l'échelle du jeu d'images, pour estimer la cohérence entre la similarité visuelle entre images et leur proximité spatiale. La troisième et dernière contribution est un processus semi-automatique de validation visuelle et de correction manuelle de la structuration d'une collection. Ce cadre exploite les approches automatiques les plus adaptées et une plateforme de visualisation basée sur une représentation en graphes. Nous utilisons plusieurs indices visuels pour orienter l'intervention manuelle de l'expert sur les zones impactantes. Cette approche semi-automatique guidée présente des avantages certains, car elle résout des erreurs de structuration qui échappent aux méthodes automatiques. Ces corrections étant ensuite largement diffusées dans toute la structure, l'améliorant globalement.Nous espérons que notre travail apportera quelques perspectives sur la structuration automatique de contenus iconographiques patrimoniaux par des approches basées sur le contenu, tout en ouvrant la porte à davantage de recherches sur la structuration semi-automatique guidée de collections d'images
This thesis explores automatic and semi-automatic structuring approaches for iconographic heritage contents collections. Indeed, exploiting such contents could prove beneficial for numerous applications. From virtual tourism to increased access for both researchers and the general public, structuring the collections would increase their accessibility and their use. However, the inherent "in silo" organization of those collections, each with their unique organization system hinders automatic structuring approaches and all subsequent applications. The computer vision community has proposed numerous automatic methods for indexing (and structuring) image collections at large scale. Exploiting the visual aspect of the contents, they are not impacted by the differences in metadata structures that mainly organize heritage collections, thus appearing as a potential solution to the problem of linking together unique data structures. However, those methods are trained on large, recent datasets, that do not reflect the visual diversity of iconographic heritage contents. This thesis aims at evaluating and exploiting those automatic methods for iconographic heritage contents structuring.To this end, this thesis proposes three distinct contributions with the common goal of ensuring a certain level of interpretability for the methods that are both evaluated and proposed. This interpretability is necessary to justify their efficiency to deal with such complex data but also to understand how to adapt them to new and different content. The first contribution of this thesis is an evaluation of existing state-of-the-art automatic content-based image retrieval (CBIR) approaches when faced with the different types of data composing iconographic heritage. This evaluation focuses first on image descriptors paramount for the image retrieval step and second, on re-ranking methods that re-order similar images after a first retrieval step based on another criterion. The most relevant approaches can then be selected for further use while the non-relevant ones provide insights for our second contribution. The second contribution consists of three novel re-ranking methods exploiting a more or less global spatial information to re-evaluate the relevance of visual similarity links created by the CBIR step. The first one exploits the first retrieved images to create an approximate 3D scene of the scene in which retrieved images are positioned to evaluate their coherence in the scene. The second one simplifies the first while extending the classical geometric verification setting by performing geometric query expansion, that is aggregating 2D geometric information from retrieved images to encode more largely the scene's geometry without the costly step of 3D scene creation. Finally, the third one exploits a more global location information, at dataset-level, to estimate the coherence of the visual similarity between images with regard to their spatial proximity. The third and final contribution is a framework for semi-automatic visual validation and manual correction of a collection's structuring. This framework exploits on one side the most suited automatic approaches evaluated or proposed earlier, and on the other side a graph-based visualization platform. We exploit several visual clues to focus the expert's manual intervention on impacting areas. We show that this guided semi-automatic approach has merits in terms of performance as it solves mistakes in the structuring that automatic methods can not, these corrections being then largely diffused throughout the structure, improving it even more globally.We hope our work will provide some first insights on automatically structuring heritage iconographic content with content-based approaches but also encourage further research on guided semi-automatic structuring of image collections

A graph consists of a set and a binary relation on that set. Each element of the set is a node of the graph, while each element of the binary relation is an edge of the graph that encodes a relationship between two nodes. Graph are pervasive in many areas of science, engineering, and the social sciences: servers on the Internet are connected, proteins interact in large biological systems, social networks encode the relationships between people, and functions call each other in a program. In these domains, the graphs can become very large, consisting of hundreds of thousands of nodes and millions of edges. Graph drawing approaches endeavour to place these nodes in two or three-dimensional space with the intention of fostering an understanding of the binary relation by a human being examining the image. However, many of these approaches to drawing do not exploit higher-level structures in the graph beyond the nodes and edges. Frequently, these structures can be exploited for drawing. As an example, consider a large computer network where nodes are servers and edges are connections between those servers. If a user would like understand how servers at UBC connect to the rest of the network, a drawing that accentuates the set of nodes representing those servers may be more helpful than an approach where all nodes are drawn in the same way. In a feature-based approach, features are subgraphs exploited for the purposes of drawing. We endeavour to depict not only the binary relation, but the high-level relationships between features. This thesis extensively explores a feature-based approach to graph vi sualization and demonstrates the viability of tools that aid in the visual ization of large graphs. Our contributions lie in presenting and evaluating novel techniques and algorithms for graph visualization. We implement five systems in order to empirically evaluate these techniques and algorithms, comparing them to previous approaches.

Att visualisera rörelsedata är ett kraftigt undersökt område och en komplex uppgift. I det här projektet har jag använt rörelsedata insamlad av sensorer från Akademiska hus utplacerade på Örebro Universitetscampus. Datan är använd för att visualisera rörelser gjorda inuti byggnaderna genom en webapplikation skriven med enbart Python. Anslutbarhet mellan sensorer är undersökt huruvida det är möjligt att generera anslutbarhetsgrafer med informationen kopplad till specifika sensorer automatiskt eller för hand. I projektet så undersöks även huruvida rörelseflöden är möjliga att visualisera via den datan tillgängliggjord av Akademiska hus.
Visualizing movement data is a heavily researched area and complex task. In this project I have used movement data collected by sensors from Akademiska hus placed on campus of Örebro University. The data is used to visualize movement made inside the buildings through a developed webapp written entirely in Python. Connectivity between sensors is studied whether it is possible to generate connectivity graphs with the information associated to specific sensors automatically or done by hand. The project also researches whether movement flows are possible to visualize with the data available from Akademiska hus.

Information visualization can be valuable in a wide range of applications, it deals with abstract, non-spatial data and with the representation of data elements in a meaningful form irrespective of the size of the data, because sometimes visualization itself focuses on the certain key aspects of the data in the representation and thus it helps by providing ease for the goal oriented interpretation. Information visualization focuses on providing a spontaneous and deeper level of the understanding of the data. Research collaboration enhances sharing knowledge and also enhances an individual’s talent. New ideas are generated when knowledge is shared and transferred among each other. According to (He et al, 2009) Research collaboration has been considered as a phenomenon of growing importance for the researchers, also it should be encouraged and is considered to be a “good thing” among the researchers. The main purpose of this thesis work is to prepare a model for the competence profile visualization purpose. For this purpose the study of different visualization techniques that exist in the field of information visualization are discussed in this thesis work. The study and discussion about the visualization techniques motivates in selecting appropriate visualization techniques for the visualization of Ontology-based competence profiles for research collaboration purpose. A proof of concept is developed which shows how these visualization techniques are applied to visualize several components of competence profile.

Data analysis techniques can be useful in decision-making processes, when patterns of interest can indicate trends in specific domains. Such trends might support evaluation, definition of alternatives, or prediction of events. Currently, datasets have increased in size and complexity, posing challenges to modern hardware resources. In the case of large datasets that can be represented as graphs, issues of visualization and scalable processing are of current concern. Distributed frameworks are commonly used to deal with this data, but the deployment and the management of computational clusters can be complex, demanding technical and financial resources that can be prohibitive in several scenarios. Therefore, it is desirable to design efficient techniques for processing and visualization of large scale graphs that optimize hardware resources in a single computational node. In this course of action, we developed a visualization technique named StructMatrix to find interesting insights on real-life graphs. In addition, we proposed a graph processing framework M-Flash that used a novel, bimodal block processing strategy (BBP) to boost computation speed by minimizing I/O cost. Our results show that our visualization technique allows an efficient and interactive exploration of big graphs and our framework MFlash significantly outperformed all state-of-the-art approaches based on secondary memory. Our contributions have been validated in peer-review events demonstrating the potential of our finding in fostering the analytical possibilities related to large-graph data domains.
Técnicas de análise de dados podem ser úteis em processos de tomada de decisão, quando padrões de interesse indicam tendências em domínios específicos. Tais tendências podem auxiliar a avaliação, a definição de alternativas ou a predição de eventos. Atualmente, os conjuntos de dados têm aumentado em tamanho e complexidade, impondo desafios para recursos modernos de hardware. No caso de grandes conjuntos de dados que podem ser representados como grafos, aspectos de visualização e processamento escalável têm despertado interesse. Arcabouços distribuídos são comumente usados para lidar com esses dados, mas a implantação e o gerenciamento de clusters computacionais podem ser complexos, exigindo recursos técnicos e financeiros que podem ser proibitivos em vários cenários. Portanto é desejável conceber técnicas eficazes para o processamento e visualização de grafos em larga escala que otimizam recursos de hardware em um único nó computacional. Desse modo, este trabalho apresenta uma técnica de visualização chamada StructMatrix para identificar relacionamentos estruturais em grafos reais. Adicionalmente, foi proposta uma estratégia de processamento bimodal em blocos, denominada Bimodal Block Processing (BBP), que minimiza o custo de I/O para melhorar o desempenho do processamento. Essa estratégia foi incorporada a um arcabouço de processamento de grafos denominado M-Flash e desenvolvido durante a realização deste trabalho.Foram conduzidos experimentos a fim de avaliar as técnicas propostas. Os resultados mostraram que a técnica de visualização StructMatrix permitiu uma exploração eficiente e interativa de grandes grafos. Além disso, a avaliação do arcabouço M-Flash apresentou ganhos significativos sobre todas as abordagens baseadas em memória secundária do estado da arte. Ambas as contribuições foram validadas em eventos de revisão por pares, demonstrando o potencial analítico deste trabalho em domínios associados a grafos em larga escala.

Virtual reality is currently in the spotlight, attracting intense scrutiny by the press, technology companies, and consumers. After twenty years of dormancy, the technology has experienced a renaissance following advancements in both hardware and software. Most of these developments, and thus press attention, have been centered on entertainment. However, if the technology is going to continue to grow, it must be useful in a wide variety of situations. This paper explores the effectiveness of visualizing data in virtual reality in comparison to traditional presentations. In this thesis, we present an approach to displaying data in a virtual reality environment. We drew upon various visualization and design principles in two dimensions (2D) and extended and implemented them in three dimensions (3D). Finally, we experimented with animations to highlight how the immersive qualities of virtual reality could help direct the user's attention to important features. To verify our work, we tested users' ability to understand worldwide economic data represented as graphs. We measured the speed and accuracy of each subject's responses as they viewed the economic data in two environments: a virtual reality (VR) environment using the Oculus Rift to look at our 3D animated chart, and a more traditional environment with a series of 2D paper charts. We concluded that for newcomers to VR, 2D charts are faster and more accurate. However, 3D visualizations are more engaging and are superior when searching for certain kinds of information such as the highest and lowest values in a set.

Esta dissertação apresenta MagnetViz, uma técnica para visualização de grafos. Enquanto a maior parte das técnicas visualizam um layout de grafo estático pre-computado, MagnetViz permite que usuários dinamicamente alterem o layout de um grafo de forma a melhor satisfazer suas necessidades. Isso é feito ao construir em cima da metáfora de física de algoritmos dirigidos à força para proporcionar aos usuários imãs virtuais, que podem atrair nodos que satisfazem um conjunto de critérios associados a eles. Critérios podem ser baseados na topologia ou semântica do grafo. Através de boundary shapes, que são simples formas geométricas que podem ser colocadas ao redor de imãs, usuários podem também definir regiões na cena onde os nodos atraídos devem permanecer. Grafos são descritos usando GraphML, uma linguagem baseada em XML, que permite a especificação dos nodos e arestas e de atributos para essas entidades. Após a submissão de um grafo como entrada, MagnetViz o exibe utilizando uma versão modificada do algoritmo clássico de Fruchterman and Rheingold, e permite que usuário, a seguir, insira imãs na cena. Usuários podem construir as condições associadas aos imãs utilizando os atributos dos nodos e arestas, além de atributos topológicos próprios de grafos. Para a avaliação de MagnetViz, foi primeiro analisado o desempenho da técnica ao ajudar usuários a executarem tarefas definidas por uma taxonomia de tarefas de visualização de grafos encontrada na literatura. Então, MagnetViz foi testada em um contexto prático através de um estudo de caso. Uma rede de co-autorias foi escolhida como conjunto de dados e o protótipo de MagnetViz foi inicialmente usado para responder questões relevantes a esses dados e então testado por um grupo de potenciais usuários, que tinham de usa-lo para responder essas mesmas perguntas. Após testar a aplicação, os sujeiotos receberam questionários sobre usas opiniões quanto a usabilidade, aplicabilidade, relevância e resultados visuais da técnica. Enquanto alguns aspectos da técnica ainda podem ser melhorados, os resultados da avaliação provaram que MagnetViz é uma abordagem válida para interação com visualizações de grafos.
This dissertation presents MagnetViz, a technique for the visualization of graphs. While most techniques visualize a static pre-computed graph layout, MagnetViz allows users to dynamically alter the layout of a graph to better satisfy their needs. This is done by building on the physics metaphor of force-directed algorithms to provide users with virtual magnets, which can attract nodes that fulfill a set of criteria associated with them. Criteria can be based on either the topology or semantics of the graph. Through boundary shapes, which are simple geometric shapes that can be placed around magnets, users can also define regions within the scene where the attracted nodes should remain. Graphs are described in GraphML, a XML-like description language which allows the specification of nodes and edges between nodes as well as attributes associated to nodes and edges. After loading a graph, Magnetviz displays it using a slightly modified version of the classical Fruchterman and Reingold' algorithm, and allows the user to insert magnets. Users can build the criteria associated with the magnets using the attributes of nodes and/or edges, besides the common graphs' topological attributes. For MagnetViz's evaluation, it was first analyzed how the technique fared in aiding users to perform tasks defined by a graph visualization task taxonomy described in the literature. Then, MagnetViz was tested within a practical context by means of a case study. A co-authorship network was chosen as the target dataset. The MagnetViz prototype was initially used to answer questions relevant to this dataset and then tested by a group of potential users, who had to use it to answer these same questions. After trying the application, subjects answered questionnaires about their opinion on the technique's usability, applicability, relevance and visual results. While some aspects of the technique should still be refined, results of the evaluation proved MagnetViz to be a valid approach when it comes to interaction with graph visualizations.

As computer science students develop more complex programs at the end of their first year of course work, comprehending the complex and varied interactions of program execution, potential control flow and data relationships become more and more difficult. Additionally, for instructors when evaluating student's programs, a simplified view of more complex (longer) programs is desirable. This thesis explores algorithms to create a tool for students that provides a simplified view of these concepts via visualization. The tool created for this thesis provides interactive visual representations of student programs. This allows for a simplified representation of the entire program along with depth exploration options to examine potential control flow and data access/mutations. The following is an exploration of program visualization, with a focus on usability in an educational setting. Two main approaches will be discussed. The first attempts to visualize the call graph of a running program by showing what methods call what other methods, and the frequency in which they are invoked. The second shows all potential paths through a non-running program, by viewing the program on a per-method level. This approach also includes information about how methods interact with data. As a test case this thesis focuses on a spell check program which builds a binary search tree dictionary then searches it for input strings and provides correction suggestions if the input is not found in the dictionary. We present an evaluation of our tool via creating visualizations of four different student implementations of this program. These visualizations are then analyzed by computer science faculty to identify common threads throughout all submissions, as well as areas where individual students struggled or excelled. Additionally visualizations are used as a tool in a lecture instructing students about binary search trees. The students provide feedback as to the effectiveness of the visualizations and their comprehension of the material. We conclude that program visualization is a difficult task, especially when students are unused to visualizing control flow. Results indicate potential for use as both a student and instructor tool, though further research is required to identify optimal usage.

Graphs have been successfully employed in avariety of problems and applications, being the object of study in modeling, analysis and construction of visual representations. While different approaches exist for graph visualization,most of them suffer from the severe clutter when the number of nodes or edges is large. Among the approaches that handle such problem, edge bundling techniques attained relative success on improving the quality of the visual representations by bending and aggregating edges in order to produce an organized layout. Despite this success, most of the exiting techniques create edge bundles based only on the visual space information, that is, there is no explicit connection between the edge bundling layout and the original data. There fore, these techniques generates less meaningful bundles and may lead users to misinterpret the data. This masters research presents a novel edge bundling technique based on the similarity relationships among vertices. We developed such technique based on two assumptions. First, it supports the hypothesis that edge bundling can better represent the data when there is an inherent connection between the proximity among the elements in the information space and the proximity between edges in the edge bundling layout. We address this question by presenting a similarity bundling framework, that considers the similarity between vertices when performing the edges bending. To guide the bundling, we create a similarity hierarchy, called backbone. This is based on a multilevel partition of the data, which groups edges of similar vertices. Second, we also support that a multiscale representation improves the visual and complexity scalability of bundling layouts. We present a multiscale edge bundling, which allows an overview plus detailed exploration, coarsening or revealing the bundling at different levelsof the same visualization. Our evaluation framework shows that our backbone produces a balanced hierarchy with a good representation of similarity relationships among vertices. Moreover, the edge bundling layout guided by the backbone reduces the visual clutter and surpass state-of-the-art techniques in displaying global and local edge patterns.
Grafos são empregados com sucesso em uma grande variedade de problemas e aplicações, sendo objeto de estudo na modelagem, análise e na construção de representações visuais. Embora existam diferentes formas para a visualização de grafos, a maioria delas sofrem pela desorganização do espaço visual quando o número de vértices ou arestas é alto. Entre as abordagens que lidam com este problema, as técnicas de agrupamentos visuais de arestas obtiveram sucesso na melhora da representação visual pelo encurvamento e agrupamento de arestas que aperfeiçoam a organização da representação. Apesar deste sucesso, a maioria das técniques criam grupos de arestas baseados apenas na informação do espaço visual, não existindo conexão explícita entre o desenho no espaço visual e o conjunto de dados original. Dessa forma, estas técnicas produzem agrupamentos de arestas com baixa significância e podem levar o usuário a uma interpretação incorreta da informação. Esta pesquisa de mestrado apresenta uma nova técnica de agrupamento visual de arestas baseado nas relações de similaridade entre os vértices. Nós desenvolvemos esta técnica com base em duas premissas. Primeiro, ela defende a hipótese que a representação por agrupamento de arestas pode representar melhor o conjunto de dados se existir uma conexão inerente entre a proximidade dos elementos no espaço de informação e a proximidade entre arestas no desenho de arestas agrupadas. Nós atendemos esta questão apresentando um arcabouço para o agrupamento de arestas baseado em similaridade, que considera a similaridade entre vértices para realizar o encurvamento das arestas. Para guiar este encurvamento, nós criamos uma estrutura de similaridade, denominada backbone. Esta estrutura é baseada em um particionamento multi-nível do conjunto de dados, que agrupa arestas de vértices similares. A segunda premissa, nós também defendemos que uma representação multiescala melhora a escalabilidade computacional e visual da representação visual de arestas agrupadas. Nós apresentamos um agrupamento visual multi-nível de arestas que permite uma exploração generalizada e detalhada, revelando detalhes em múltiplos níveis da visualização. Nosso processo de avaliação mostra que a construção do backbone produz uma hierarquia balanceada e com boa representação das relações de similaridade entre os vértices. Além disso, a visualização com arestas guiadas pelo backbone reduz a desordem visual e melhora as técnicas do estado-da-arte na identificação de padrões de arestas globais e locais.

The Rigi research group has built an SVG (Scalable Vector Graphics) Graph Visualization Engine to visualize, interactively explore and annotate software structures. The biggest difficulty we experienced during the customization of this engine for different domains is that we need to create idiosyncratic generators every time. Each generator is created by different developers using different approaches. If the subject information model does not contain layout information, developers of the generator are also responsible for writing algorithms to calculate layout. In this thesis, we present a customization framework for our SVG Graph Visualization Engine to provide flexible customization using third party libraries to construct specific SVG Graph Generators. The customization framework consists of documentation for the existing graph engine and a componentized abstract generator that can be extended for a variety of information domains. We also created two reference implementations for the abstract generator and included them in the template solution project to exemplify the usage of the customization framework. We also validated the template solution with an end user to build a generator for a new domain. Our customization framework greatly eases the process of building SVG Editor Generators for domain-specific visualization engine.

Algorithm has always been a challenging topic for students to learn because of its high level of abstraction. To provide visual aid for algorithm education, many algorithm visualization systems have been designed, developed, and evaluated for the last two decades. However, neither the topics covered nor the interactivity of most AV systems are satisfying. This problem is presented in detail in chapter 2. As a result, this research aims to design, implement and evaluate a compiler-based algorithm visualization system on complex graph algorithm implementation with the assumption that it can help students build both confidence and competence in understanding it. This system is designed and developed according to the method in chapter 3. To test the hypothesis, a comparison experiment on 10 students in the Computer Graphics Technology department is conducted. The complete test protocol can be found in chapter 3.4, and the result can be found in chapter 4. Based on the limited number of subjects’ testing data, a rough conclusion is made that this AV system has only a slight positive effect on subjects’ confidence and competence in understanding complex graph algorithm’s implementation, and its usability is acceptable. However, a concrete conclusion can only be reached if the testing is conducted to a larger group of subjects. In addition to the objective testing data, some interesting subjective observations, which are listed in chapter 5.2 are also made while doing the test. These observations indicate that algorithm visualization may more of a tool to examine users’understanding of the implementation than a tool to help them learn it.

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Artificial Intelligence and automation have significantly expanded as a research field, and gradually play an increasingly important role in business decision-making. The goal of this internship is to implement a highly customizable library as part of an Automated Machine Learning system, which intends to give accessibility of data-driven decisions to less experienced users, and let them perform in-depth analyzes on large quantities of data. This library is built upon the Markov chain theory, which has several real-world applications and also serves as the basis for other theories with a higher level of complexity. In addition to the library, a visualization tool is also conceived to facilitate the use of the library by providing an interface to process parameter settings and display modeling data in the form of interactive directed graphs.

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
The octilinear schematic map, layout recognized worldwide in metro maps, is an important transit informative tool. This research investigates how algorithms for the visualization of schematic maps can be availed in mobile web devices context in order to empower the efficiency in transmitting information of bus transit maps. A genetic algorithm for path octilinear schematization technique has been used and tested to create the schematic data. Location-based and interactivity functionalities were embedded to the resulting digital maps in order to create personalized maps to meet specific user needs. A prototype of a web application and real transit data of the city of Castellón in Spain was used to test the methodology. The results have shown that real time schematizations open possibilities concerning usability that add extra value to schematic transit maps. Additionally, suggested improvements have been made to the genetic algorithm and performance tests show that genetic algorithms are adequate, in terms of efficiency, to sketch bus transit maps automatically.