Unit MODELS AND ALGORITHMS FOR DATA VISUALIZATION
- Course
- Computer engineering and robotics
- Study-unit Code
- A003168
- Curriculum
- Data science e data engineering
- Teacher
- Giuseppe Liotta
- Teachers
-
- Giuseppe Liotta
- Alessandra Tappini (Codocenza)
- Hours
- 62 ore - Giuseppe Liotta
- 10 ore (Codocenza) - Alessandra Tappini
- CFU
- 9
- Course Regulation
- Coorte 2024
- Offered
- 2025/26
- Learning activities
- Caratterizzante
- Area
- Ingegneria informatica
- Academic discipline
- ING-INF/05
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English
- Contents
- Introduction to data visualization and its applications. Introduction to the design of the visual analysis of data. Nested model and linearized model. Data modeling, task modeling, visual perception principles, visual idioms, interaction design, technological choices, libraries and GUIs. Algorithms for visual analytics: tables, spatial data, and relational data. Algorithms for graph drawing: trees, straight-line drawings, polyline drawings, and orthogonal drawings.
- Reference texts
- - T. Munzner, Visualization Analysis and Design. A K Peters Visualization Series, CRC Press, 2014. - G. Di Battista, P. Eades, R. Tamassia and I. Tollis, Graph Drawing: Algorithms for the Visualization of Graphs. Prentice-Hall, 1999.
- Educational objectives
- Students will learn the principles and the algorithmic technologies to design novel systems of visual analytics, which has a wide variety of applications in classical application areas (e.g., circuit design, software engineering, networking, bioinformatics) and it is central for data science, an emerging area of growing industrial interest.
- Prerequisites
- The course is devoted to graduate students at the second year of their masters degree. Students are expected to know the fundamentals of computer programming, computer architectures, operating systems, algorithms and data structures, theory of optimization and control, complexity theory and languages, data bases, software engineering, computer graphics.
- Teaching methods
- There are three types of lectures: Lectures devoted to the theory of information visualization. Students can download the slides of each lecture of this type before coming to class. Case studies, where theory is applied to concrete examples and exercises . Lectures about the practice of information visualizations.
- Other information
- Coming to classes is not mandatory but it is strongly recommended. The teacher experience is that almost all students who come to class pass the exam right at the end of the class and with marks above average; those who do not come to class regularly may have a harder time passing the exam. Office hours: every Tuesday from 6:00 p.m. to 7:30 p.m..
- Learning verification modality
- Oral examination of theoretical and applied nature, including also the discussion of a project. Duration: 30-45 minutes. Score: 30/30. Objective: evaluating the ability of using and designing technologies and algorithms for visual analytics, based on the topics taught in class.
- Extended program
- The course is offered at the graduate level. It addresses the main aspects of information visualization and covers fundamental principles of algorithm engineering. We report below, for each lecture, the covered topic. Each topic is covered in one or more lectures lecture; each lecture consists of two hours. Lect.1 Introduction to information visualization. Lect.2 Design models: nested model and linearized model. Lect.3 Data modeling. Lect.4 Task modeling. Lect.5 Perception principles. Lect.6 Visual idioms (part I). Lect.7 Visual idioms (part II). Lect.8 Interaction design. Lect.9 Technological choices. Lect.10 Libraries and GUIs. Lect.11 Convex hull and 1D range search. Lect.12 2D range search Lect.13 Blocks. Lect.14 Treemaps. Lect.15 Drawings of trees (part I). Lect.16 Drawings of trees (part II). Lect.17 Force directed methods (part I). Lect.18 Force directed methods (part II). Lect.19 Polyline drawings of directed graphs (part I). Lect.20 Polyline drawings of directed graphs (part II). Lect.21 Polyline drawings of directed graphs (part III). Lect.22 Polyline drawings of directed graphs (part IV). Lect.23 Introduction to planarity. Lect.24 Planarity testing and planarization. Lect.25 Flow techniques. Lect.26 Orthogonal drawings (part I). Lect.27 Orthogonal drawings (part II). Lect.28 Orthogonal drawings (part III). Lect.29 Orthogonal drawings (part IV). Lect.30 Orthogonal drawings (part V). Lect.31 Edge length minimization. Lect.32 Project example. Lect.33 Introduction to D3.js. Lect.34 D3 - working with data (part I). Lect.35 D3 - working with data (part II). Lect.36 D3 - force directed.
- Obiettivi Agenda 2030 per lo sviluppo sostenibile