Unit INFORMATION VISUALIZATION AND VISUAL ANALYTICS
- Course
- Computer engineering and robotics
- Study-unit Code
- 70A00028
- Curriculum
- Data science
- Teacher
- Giuseppe Liotta
- Teachers
-
- Giuseppe Liotta
- Emilio Di Giacomo (Codocenza)
- Hours
- 60 ore - Giuseppe Liotta
- 12 ore (Codocenza) - Emilio Di Giacomo
- CFU
- 9
- Course Regulation
- Coorte 2021
- Offered
- 2022/23
- 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
- Italian
- Contents
- The main topics of the course can be summarized as follows. The architecture of an information visualization system. Paradigms for graph Drawing. Divide and conquer techniques and their applications to information visualization. Planar orientations and visibility drawings. Network flow optimization and orthogonal drawings. Force-directed methods. Straight-line drawings of planar graphs. Integer linear programming and layered drawings of hierarchical information spaces.
- Reference texts
- - G. Di Battista, P. Eades, R. Tamassia and I. Tollis, "Graph Drawing: Algorithms for the Visualization of Graphs", Prentice-Hall 1999
- T. Nishizeki, Md. S. Rahman "Planar Graph Drawing", World Scientific, Lecture Notes Series On Computing, Vol. 12, 2004 - Educational objectives
- Studemts will learn the principles and the algortihmic technologies to design novel systems of vsual analytics, which has a wide variety of applications in classical appliaction areas (e.g. circuit design, software engineering, networking, biioinformatica) and it is central for data science, an emerging area of growing industrial interest.
- Prerequisites
- The course is devoted to graduate sstudents 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 slided of each lecture of this type before coming to class.
Case studies,where the theory is applied to concrete examples and exercises similar to those given during the final test are discussed.
Lectures about the practice of information visualizations. These lectures are devoted to info-vis software development and some of them are given using the computer lab. - 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.
Two years ago, the average score of the students was 26; it was 27.6 last year. The average score is 27.8 this year.
Office hours every Teusday from 6:00 p.m. to 7:30 p.m.. Students are welcome to meet the teacher outside office hours upon e-mail exchange to shedule the meeting. - Learning verification modality
- The examination consists of an oral test
Oral test
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.
The results of the exam are presented by the teacher to the student at the end of the exam a short discussion. - 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. Its target audience are post graduate students at the first year of their Masters Degree in Computer Engineering (Laurea Magistrale in Ingegneria Elettronica e dell'Informazione). Lectures are given in Italian. We therefore report below, for each lecture, the covered topic in Italian. Each topic is covered in one or more lectures lecture; each lecture cosnists of two hours in the classoroom/lab.
Lect. 1 Introduzione alla visualizzazione dell'informazione. Obiettivi del corso; terminologia di base.
Lect.2 Richiami (proma parte)
Lect.3 Richiami (seconda parte)
Lect.4 Disegni layered di alberi
Lect.5 Disegni radiali e HV
Lect.6 Recursive Winding
Lect.7 Applicazone di tecniche divide et impera alla visualizazzione di reti (prima parte)
Lect.8 Treemap
Lect.9 Disegni di grafi serie-parallelo
Lect.10 Applicazone di tecniche divide et impera alla visualizazzione di reti (seconda parte)
Lect.11 Test planarità
Lect.12 Applicazone di tecniche divide et impera alla visualizazzione di reti (terza parte)
Lect.13 Planarizzazione
Lect.14 Richiami sulle tecniche di flusso
Lect.15 Disegni Ortogonali (prima parte)
Lect.16 Disegni Ortogonali (seconda parte)
Lect.17 Disegni Ortogonal (terza parte)
Lect.18 Disegni Ortogonali (quarta parte)
Lect.19 Disegni Ortogonali (quinta parte)
Lect.20 Applicazioni delle tecniche per la visualizzazione di reti ortogonali
Lect. 21 Metodi force directed (prima parte)
Lect. 22 Metodi force directed (seconda parte)
Lect. 23 Applicazioni delle tecniche froce directed
Lect.24 Ordinamento Canonico e disegni in area ottimale
Lect.25 Shift algorithm
Lect. 26 st-grafi ed st-orientazione
Lect. 27 Rappresentazioni di visibilità
Lect.28 Disegni poligonali
Lect.29 Disegni di dominanza
Lect.30 Disegni di grafi orientati (prima parte)
Lect. 31 Disegni di grafi orientati (seconda parte)
Lect.32 Disegni di grafi orientati (terza parte)
Lect. 33 Disegni di grafi orientati (quarta parte)
Lect.34 Progettazione di librerie algortimiche per la visualizzazione dell'informazione
Lect.35 Visual analytics in partica (esperienze progettuali su temi come smart cities, data science, semantic web)
Lect.36 Ricapitolazione del corso e conlcusioni