Gli obiettivi del corso sono quelli di introdurre lo studente ai problemi e alle soluzioni relative all'esplorazione visuale di dati astratti, con particolare enfasi sui fenomeni della percezione visiva,sulle metafore grafiche che possono essere adottate e sui metodi e modelli algoritmici più comunemente utilizzati. Verranno approfondite le
conoscenze degli studenti su problemi di ingegneria degli algoritmi e diottimizzazione su reti. Tali conoscenze verranno applicate a problemi di visualizzazione dell'informazione di varia natura e con una forte connotazione pratica.
conoscenze degli studenti su problemi di ingegneria degli algoritmi e diottimizzazione su reti. Tali conoscenze verranno applicate a problemi di visualizzazione dell'informazione di varia natura e con una forte connotazione pratica.
Curriculum
scheda docente materiale didattico
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.
Per scaricare le slides sono neccessarie delle credenziali da richiedere al docente (maurizio.patrignani@uniroma3.it)
Programma
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.
Testi Adottati
Trasparenze fornite dal docente e scaricabili via via dal sito del corso: http://www.dia.uniroma3.it/~infovis/Per scaricare le slides sono neccessarie delle credenziali da richiedere al docente (maurizio.patrignani@uniroma3.it)
Modalità Valutazione
Un progettino individuale in D3.js durante l'erogazione del corso (10% della valutazione globale). Un breve compito scritto di mezzora su un argomento del corso scelto in una lista di otto tematiche (20% della valutazione). Un progetto finale, possibilmente svolto in gruppo, e una discussione di quest'ultimo (70%). scheda docente materiale didattico
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.
Per scaricare le slides sono neccessarie delle credenziali da richiedere al docente (maurizio.patrignani@uniroma3.it)
Mutuazione: 20802126 VISUALIZZAZIONE DELLE INFORMAZIONI in Ingegneria informatica LM-32 PATRIGNANI MAURIZIO
Programma
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.
Testi Adottati
Trasparenze fornite dal docente e scaricabili via via dal sito del corso: http://www.dia.uniroma3.it/~infovis/Per scaricare le slides sono neccessarie delle credenziali da richiedere al docente (maurizio.patrignani@uniroma3.it)
Modalità Valutazione
Un progettino individuale in D3.js durante l'erogazione del corso (10% della valutazione globale). Un breve compito scritto di mezzora su un argomento del corso scelto in una lista di otto tematiche (20% della valutazione). Un progetto finale, possibilmente svolto in gruppo, e una discussione di quest'ultimo (70%). scheda docente materiale didattico
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.
Per scaricare le slides sono neccessarie delle credenziali da richiedere al docente (maurizio.patrignani@uniroma3.it)
Mutuazione: 20802126 VISUALIZZAZIONE DELLE INFORMAZIONI in Ingegneria informatica LM-32 PATRIGNANI MAURIZIO
Programma
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.
Testi Adottati
Trasparenze fornite dal docente e scaricabili via via dal sito del corso: http://www.dia.uniroma3.it/~infovis/Per scaricare le slides sono neccessarie delle credenziali da richiedere al docente (maurizio.patrignani@uniroma3.it)
Modalità Valutazione
Un progettino individuale in D3.js durante l'erogazione del corso (10% della valutazione globale). Un breve compito scritto di mezzora su un argomento del corso scelto in una lista di otto tematiche (20% della valutazione). Un progetto finale, possibilmente svolto in gruppo, e una discussione di quest'ultimo (70%).