The goal of this course is that of introducing the participants to the problems and the solutions in the area of the visual exploration of abstract data, with a particular emphasis on the visual perception phenomena, on the graphic metaphors that can be exploited and on thealgorithmic methods and models that can be adopted. The
knowledge of the participants about algorithm engineering and network optimization problems will be deepened. Such a knowledge will be applied to different strains of visualization problems with a strong practical approach.
knowledge of the participants about algorithm engineering and network optimization problems will be deepened. Such a knowledge will be applied to different strains of visualization problems with a strong practical approach.
Curriculum
teacher profile teaching materials
Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.
Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.
Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.
Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.
Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.
Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).
Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.
Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.
Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.
Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.
Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.
Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.
Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment
Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.
Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.
Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.
Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)
Programme
Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.
Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.
Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.
Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.
Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.
Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).
Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.
Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.
Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.
Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.
Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.
Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.
Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment
Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.
Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.
Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.
Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.
Core Documentation
Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)
Type of evaluation
An individual small project using D3.js during the course (10% of the final grade). A written essay of half an hour about one topic chosen in a set of eight (20% of the final grade). A bigger project, preferably a group project, and an oral discussion of it (70%). teacher profile teaching materials
Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.
Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.
Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.
Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.
Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.
Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).
Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.
Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.
Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.
Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.
Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.
Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.
Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment
Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.
Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.
Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.
Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)
Mutuazione: 20802126 VISUALIZZAZIONE DELLE INFORMAZIONI in Ingegneria informatica LM-32 PATRIGNANI MAURIZIO
Programme
Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.
Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.
Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.
Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.
Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.
Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).
Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.
Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.
Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.
Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.
Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.
Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.
Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment
Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.
Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.
Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.
Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.
Core Documentation
Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)
Type of evaluation
An individual small project using D3.js during the course (10% of the final grade). A written essay of half an hour about one topic chosen in a set of eight (20% of the final grade). A bigger project, preferably a group project, and an oral discussion of it (70%). teacher profile teaching materials
Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.
Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.
Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.
Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.
Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.
Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).
Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.
Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.
Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.
Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.
Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.
Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.
Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment
Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.
Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.
Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.
Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)
Mutuazione: 20802126 VISUALIZZAZIONE DELLE INFORMAZIONI in Ingegneria informatica LM-32 PATRIGNANI MAURIZIO
Programme
Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.
Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.
Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.
Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.
Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.
Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).
Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.
Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.
Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.
Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.
Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.
Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.
Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment
Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.
Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.
Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.
Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.
Core Documentation
Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)
Type of evaluation
An individual small project using D3.js during the course (10% of the final grade). A written essay of half an hour about one topic chosen in a set of eight (20% of the final grade). A bigger project, preferably a group project, and an oral discussion of it (70%).