Scientific Data
Visualization
2 - Iulie - 2019
Planificari Exam. & Restante
(toti):
·
24.06.2019
(Luni, ora 9:o) Exam. Data 1
·
26.06.2019
(Miercuri, ora 9:oo) Exam. Data 2
· 11.07.2019 (Joi, ora 14:oo, C310) Restanţe
§ CALCUL DE ÎNALTĂ PERFORMANŢĂ
ŞI ANALIZA VOLUMELOR MARI DE DATE
§ INTELIGENŢĂ
COMPUTAŢIONALĂ APLICATĂ
§ BAZE
DE DATE
§ INGINERIE
SOFTWARE
Tematica
pentru Lucrarea Scrisă ~ Vizualizarea științifică
a datelor
Joi, 11 iulie ~ C310 ~ 14oo-17oo
|
Sub.
a) sau b) |
Curs |
Titlul cursului |
|
1 |
1 |
Prezentare Generală – Etape,
Modelare, Simulare, … |
|
2 |
2 |
Modelling, Simulation, Visualization |
|
3 |
4 |
Model Validation |
|
4 |
8 |
Scientific Visualization |
|
5 |
10 |
Interactive Simulation and Visualization |
|
6 |
11 |
Interactive Simulation, and Visualization ~
Tools for Desig |
|
7 |
12 |
Methods for visualizing two- and
three-dimensional data sets |
|
Sub.
|
Pct. |
Sub.
a) sau b) |
|
Of. |
2 |
-1p, -2p Sch. Sub. |
|
a) |
4 |
1
à 7 ≠ b) |
|
b) |
4 |
1
à 7 ≠ a) |
Cursuri
Curs 8 – 17.04. 2019– Scientific Visualization
O r a r
|
Zi |
Ora |
Fr. |
Sala |
Tip |
|
Miercuri |
16~18 |
|
L339 |
Sem. |
|
18~20 |
|
C335 |
Curs |
Anunţ:
-
Temele
pentru Referate şi Proiecte se vor planifica până la data de
8.05.2019!
-
Susţinerea
Referatelor şi Prezentare Proiectelor se va face până la data de
15.05.2019 (După
sustinere, referatele se vor trimite pe mail).
PROGRAMAREA SESIUNII 12
Iunie - 2 Iulie 2017 Grupa 242
|
Vizualizarea ştiinţifică a datelor |
Data şi ora |
Sala |
|
Examen ( * Rezultate * ) -_- |
Luni,
26.06.2017 ora 1600 |
7/I |
|
Restante |
Marti,
11.07.2017 ora 1600 |
C512 |
Tematica
pentru Lucrarea Scrisa
Planificarea sustinerii referatului
|
Nr. |
Nume Student |
Grupa |
Titlu - Referat |
Data Ref. |
Den. – Aplicatie (19.05.2012) |
|
1 |
|
|
Modelling in Guessing Game |
1.04.2017 |
Modelling in Guessing Game - Den. – Aplicatie ? |
|
2 |
|
|
Visualization techniques in Simulation |
1.04.2017 |
Visualization of Earthquake Simulation |
|
3 |
|
|
Visualization in Web_mining |
1.04.2017 |
BlogIT : discovering patterns from blog collections |
|
4 |
|
|
Web mining - studying (behavior in) social media |
1.04.2017 |
Web mining - studying (behavior in) social media - Den. – Aplicatie ? |
|
5 |
|
|
Simularea fluxului de călători dintr-o staţie CFR |
… |
Simularea fluxului de călători dintr-o staţie CFR - Den. – Aplicatie ? |
|
6 |
|
|
Statistical Methods - Linear Regression Analysis |
|
Statistical Methods - Linear Regression Analysis - Den. – Aplicatie ? |
|
7 |
|
|
Traffic Simulation of Cluj-Napoca's City Center |
|
Traffic Simulation of Cluj-Napoca |
|
8 |
|
|
Fluid dynamic simulations |
|
Den. – Aplicatie ?? |
|
9 |
|
|
Disease Spread Simulation based on SIR Model |
|
Den. – Aplicatie ?? |
|
10 |
|
|
... |
|
|
|
… |
|
|
|
|
|
syllabus
1.
Information regarding the programme
|
1.1 Higher education institution |
Babeş Bolyai University |
|
1.2 Faculty |
Faculty of Mathematics and Computer Science |
|
1.3 Department |
Department of Computer Science |
|
1.4 Field of study |
Computer Science |
|
1.5 Study cycle |
Master |
|
1.6 Study programme / Qualification |
Applied Computational Intelligence |
2.
Information regarding the discipline
|
2.1
Name of the discipline |
Scientific Data Visualization |
|||||||
|
2.2
Course coordinator |
Lecturer Professor PhD. Prejmerean Vasile |
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|
2.3
Seminar coordinator |
Lecturer Professor PhD. Prejmerean Vasile |
|||||||
|
2.4. Year of study |
1 |
2.5 Semester |
2 |
2.6. Type of evaluation |
E |
2.7 Type of discipline |
Compulsory |
|
3. Total
estimated time (hours/semester of didactic activities)
|
3.1
Hours per week |
3 |
Of which: 3.2 course |
2 |
3.3 seminar/laboratory |
1 |
||
|
3.4 Total hours in the curriculum |
42 |
Of which: 3.5 course |
28 |
3.6 seminar/laboratory |
14 |
||
|
Time
allotment: |
hours |
||||||
|
Learning
using manual, course support, bibliography, course notes |
36 |
||||||
|
Additional
documentation (in libraries, on electronic platforms, field documentation) |
36 |
||||||
|
Preparation
for seminars/labs, homework, papers, portfolios and essays |
36 |
||||||
|
Tutorship |
18 |
||||||
|
Evaluations |
18 |
||||||
|
Other
activities: Project |
14 |
||||||
|
3.7
Total individual study hours |
158 |
|
|||||
|
3.8
Total hours per semester |
200 |
|
|||||
|
3.9
Number of ECTS credits |
8 |
|
|||||
4.
Prerequisites (if necessary)
|
4.1.
curriculum |
·
Ability to work with
an integrated development environment |
|
4.2.
competencies |
·
Average programming skills in a visual programming language |
5.
Conditions
(if necessary)
|
5.1. for the course |
·
An LCD projector |
|
5.2. for the seminar /lab activities |
·
Laboratory with twelve computers; high level
programming language environment |
6. Specific competencies acquired
|
Professional competencies |
·
Ability to apply
knowledge of computing and mathematics appropriate to the discipline; ·
Ability to analyze a
problem, and identify and define the computing requirements appropriate to
its solution; ·
Ability to identify
and to specify computing requirements of an application and to design,
implement, evaluate, and justify computational solutions; ·
Ability to use
current techniques and skills to integrate available theory and tools
necessary for applied computing practices. |
|
Transversal
competencies |
·
Ability to apply
mathematical foundations, algorithmic principles, and computer science
theory; ·
Ability to apply
design and development principles in the construction of software systems; ·
Ability to acquire knowledge
properly in an application domain in the modeling and design; ·
Ability to work
effectively in a team. |
7.
Objectives of the discipline (outcome of the acquired competencies)
|
7.1 General objective of the
discipline |
·
Be able to apply theories, principles and concepts with technologies
to design, develop, and verify computational solutions; ·
Be able to use data visualization
(technique tool used to help researchers understand and/or interpret data) |
|
·
To assimilate data visualization techniques and the visualization as a
method of studying the real phenomenon. To gain skills related to problem
solving through visualization of data. ·
To teach the students the concepts used in the field of modeling and
visualization of simulation and to acquire the methods for validation of
simulation using Scientific Data Visualization. ·
After promotion the students should be able to use data visualization
as a method of solving real problems. |
8. Content
|
8.1 Course |
Teaching methods |
Remarks |
|
1. Scientific Data - data-formats used in
science or engineering referred as scientific data; - scientific data as massive and digital
data with a variety of data formats - floating-point data, integer data,
image data, and clip data; - format and data dimensions (1-D, 2-D,
3-D, …) |
Expositions: description, explanation, class lectures, Use of problems:
use of problem questions, problems and problem situations. Other methods:
company examples. |
|
|
2. Data Visualization - technique tool used to help researchers
understand or interpret data;
- similar techniques used in
other visualization;
- data analysis methods and
techniques. |
Expositions: description, explanation, dialog-based
lectures, current lectures, Use of problems:
problems and problem situations. |
|
|
3. Visualization Techniques (part I) - plotting (data analysis)
- mapping (graphics) - color image interpreting (image processing) - volume rendering (volume visualization) |
Expositions: description, explanation, class lectures,
dialog-based lectures, current lectures. Other methods:
case study; company examples, discussion of material. |
|
|
4. Visualization Techniques (part II) - graphics (Glut, OpenGL, …)
- animation - virtual reality (CaveLib, openGL, …) - internet
- database and data management |
Expositions: description, explanation, class lectures,
dialog-based lectures, current lectures. Use of problems:
use of problem questions, problems and problem situations. |
|
|
5. Data Visualization Tools - Data Visualization
Software;
- Basic TecPlot guide. |
Expositions: description, explanation, class
lectures. Other methods:
discussion of material |
|
|
6.
Current issues in scientific visualization
- scientific visualization models; - validation visualization; - design for scientific
visualization. |
Expositions: description, explanation, class lectures,
dialog-based lectures, lectures. Other methods: discussion
of material. |
|
|
7.
Data modeling
- data representation; - modeling volumes; - unevenly distributed data modeling; - modeling by triangulation. |
Expositions: description, explanation, class lectures,
dialog-based lectures, lectures. Use of problems:
use of problem questions |
|
|
8.
Visual interactive simulation - what is simulation,
when to use simulation, types of modeling and simulation, advantages of
simulation, the steps of a simulation study.
- visualization techniques for validation.
|
Expositions: description, explanation, introductive
lectures, Other methods:
case study; company examples. |
|
|
9. Visual interactive modeling and problem solving - visual interactive
models - sensitivity analysis, calibration,
input-output data analysis for simulations |
Expositions: description, explanation, class lectures, Use of problems:
use of problem questions. |
|
|
10. Techniques
needed for data visualization - applications of visualization; - data analysis and visualization; - visualizing
multidimensional data; - data visualization unevenly distributed. |
Expositions: description, explanation, dialog-based
lectures, current lectures, Use of problems:
problems and problem situations. |
|
|
11. Visualization
techniques (part I) - constructing
isosurfaces, direct volume rendering, streamlines, streaklines, and
pathlines, table, matrix, charts (pie chart, bar chart, histogram, function
graph, scatter plot, etc.), graphs (tree diagram, network diagram, flowchart,
existential graph, etc.), maps. |
Expositions: description, explanation, class lectures,
dialog-based lectures, current lectures. Other methods:
case study; company examples, discussion of material. |
|
|
12. Visualization
techniques (part II) - parallel coordinates -
a visualization technique aimed at multidimensional data, treemap - a
visualization technique aimed at hierarchical data, Venn diagram, Timeline,
Euler diagram, Chernoff face, Hyperbolic trees, brushing and linking, Cluster
diagram or dendrogram, Ordinogram |
Expositions: description, explanation, class lectures,
dialog-based lectures, lectures. Conversations:
conversations for knowledge consolidation, conversations to systematize and
synthesize. Other methods:
discussion of material. |
|
13. Interactive
simulation and visualization applications - Automatic 3-D
animation and visualization - Interactive 3-D Model Construction -
Surgical Simulation - 3D
MRI Acquisition and Visualization - Virtual Morphological Modelling |
Expositions: description, explanation, class lectures,
dialog-based lectures, current lectures. Use of problems:
use of problem questions, problems and problem situations. |
|
|
14.
Data visualization in Business Analytics
(visual technologies, and data visualization). - Visual analysis,
scorecards, dashboards, 3D virtual reality. |
Expositions: description, explanation, class lectures. Use of problems:
use of problem questions. |
|
|
Bibliography 1.
Arsham H., Systems
Simulation: The Shortest Path from Learning to Applications, http://www.ubmail.ubalt.edu/~harsham/simulation/sim.htm
2.
Averill M. Law and W.
David Kelton, Simulation Modeling and Analysis, McGraw Hill, Third Edition
(2000). 3.
Daniel Hennessey,
Algorithms for the Visualization and Simulation of Mobile Ad Hoc and
Cognitive Networks - A Thesis Submitted to the Faculty of Drexel University –
by Daniel Hennessey in partial fulfillment of the requirements for the degree
of Master of Science in Computer Science, June 2009, http://idea.library.drexel.edu/bitstream/1860/3028/1/Hennessey_Daniel.pdf 4.
Dodescu Gh.,
Simularea sistemelor, Ed.Militara, Bucuresti, 1986. 5.
Fernando P. Birra,
Manuel J. Prsospero, SiPaViS -A Toolkit for Scientific Visualization and
Simulation, Computer Science Department, New University of Lisbon, P-2825
Monte Caparica, Portugal, emails: fpb@di.fct.unl.pt, Journal for Geometry and Graphics, Volume 3
(1999), No. 1, 47{55, ps@di.fct.unl.pt, http://www.heldermann-verlag.de/jgg/jgg01_05/jgg0304.pdf 6.
Helmut Doleisch and
Helwig Hauser, Smooth Brushing for Focus+Context, Visualization of Simulation
Data in 3D, VRVis Research Center in Vienna, Austria, mailto: Doleisch, Hauser@VRVis.at,
http://www.VRVis.at/vis/http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.18.2536&rep=rep1&type=pdf 7.
Miller A. and Allen P. , Santos V., and Valero-Cuevas
F., From robotic hands to human hands: a visualization and simulation engine
for grasping research, http://www.cs.columbia.edu/~allen/PAPERS/industrialrobot.pdf 8.
Popescu, G. D.,
Radoiu, D., Elemente de procesare digitala a informatiei, Universitatea
Babes-Bolyai, Cluj Napoca, Facultatea de Fizica, 146 pag., 2000 9.
Rădoiu, D.,
Popescu, G. D., Vizualizarea stiintifica a datelor experimentale, Editura
Universitatii Petru Maior, 168 pag., ISBN 973-8084-05-9, 2000 10.
Rădoiu D.,
Scientific Visualization; Editura "Casa Cărţii de
Ştiinţă", Cluj-Napoca, 150 pag., ISBN 973-686-645-9,
2004; 11.
Rodt T., Schlesinger A., Schramm A., Diensthuber M., Rittierodt M.,
Krauss J.K., 3D visualization and simulation
of frontoorbital advancement in metopic synostosis, http://www.slicer.org/publications/item/view/1513 12.
Rosenblum, L., R.
Earnshaw, J. Encarnação, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F.
Post, D. Thalmannn, Scientific
Visualization, Advances and
Challenges, IEEE Computer Society Press, Academic Press, 1994 13.
Spence,
R., Information Visualization, Addison Wesley, 2001 14.
Stephen Few, Data
Visualization Past, Present, and Future, January 10, 2007. http://www.perceptualedge.com/articles/Whitepapers/Data_Visualization.pdf 15.
VADUVA I., Modele de
simulare cu calculatorul, Ed. Tehnica, Bucuresti 1977. 16.
Win Cho Aye, Malcolm
Yoke Hean Low, Huang Shell Ying, Hsu Wen Jing, Liu Fan, Zeng Min,
Visualization and Simulation Tool for Automated Stowage Plan Generation
System, http://www.iaeng.org/publication/IMECS2010/IMECS2010_pp1013-1019.pdf |
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|
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|
8.2 Seminar |
Teaching methods |
Remarks |
|
|
1.
|
The first two seminars are dedicated to
surveying information sources available on Internet and Intranet, and planning
of the papers and projects. |
Expositions: description, explanation, introductive
lectures. Conversations: debate, dialog, introductive conversations. Other methods: individual study, exercise, homework
study. |
|
|
2.
|
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|
3.
|
The next nine seminars (from three to eleven)
are dedicated to paper presentations. |
Conversations: debate,
dialog, conversations for knowledge consolidation, conversations to
systematize and synthesize knowledge. Use of problems:
use of problem questions, problems and problem situations. Discovery: directed and independent rediscovery,
creative discovery, discovery by documenting. Other methods: case study; cooperation, individual
study, homework study, company examples, discussion of material. |
|
|
4.
|
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|
… |
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|
10.
|
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|
11.
|
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|
12.
|
The project demos will be scheduled in the last three seminars. |
Conversations: debate, dialog. Discovery: discovery by documenting. ther methods: discussion of material. |
|
|
13.
|
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|
14.
|
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|
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|
Bibliography 1.
Beatriz Sousa Santos,
Introduction to Data and Information Visualization, Universidade de Aveiro
Departamento de Electrónica, Telecomunicações e Informática, Universidade de
Aveiro, 2010 http://www.ieeta.pt/~bss/MAPI/Introduction-to-Vis-5-10.pdf 2.
Brodlie, K., L.
Carpenter, R. Earnshaw, J. Gallop, R. Hubbold, A. Mumford, C. Osland, P.
Quarendon, Scientific
Visualization, Techniques and Applications, Springer Verlag, 1992 3.
Card, S., J. Mackinlay,
B. Schneiderman (ed.), Readings in Information Visualization- Using
Vision to Think, Morgan Kaufmann, 1999 5.
Jack P.C. Kleijnen,
Five-stage procedure for the evaluation of simulation models through
statistical techniques, Proceedings of the 1996 Winter Simulation Conference,
p.248-254. 7.
Keller, P., M.
Keller, Visual Cues, IEEE
Computer Society Press, 1993 8.
Kleijnen J.P.C.,
Sensitivity analysis and optimization, Proceed. of the 1995 Winter
Simulation Conference, p.133-140,
19959. 9.
Kleijnen J.P.C.,
Validation of models: statistical techniques and data availability, Proceed.
of the 1999 Winter Simulation
Conference, 1999. 10. Lichenbelt, B., R. Crane, S. Naqvi, Introduction to Volume Rendering,
Prentice Hall, 1998 11.
Sanderson D.P.,
R.Sharma, R.Rozin, and S.Treu, The Hierarchical Simulation Language HSL: A
Versatile Tool for Process-Oriented Simulation, ACM Trans.on Modeling and
Computer Simulation, Vol.1, no.2, 1991, pp.113-153. 12. Schroeder, W., K.
Martin, B. Lorensen, The
Visulization Toolkit- An Object Oriented Approach to 3D Graphics, 2nd ed., Prentice Hall, 1998 13.
SCOR_2006_visualization,
Data Visualization, http://www.scor-int.org/Project_Summit_2/SCOR_2006_visualization.pdf 14. Shermer, M., “The Feynman-Tufte Principle”, Scientific American, April 2005, pp.
38 15.
T.I. Oren, Concepts
and Criteria to Asses Acceptability of Simulation Study: a frame of
reference, Comm.ACM, vol.24(1981), no.4, 180-184. 19. Tufte, E., The Visual Display of Quantitative
Information, Graphics Press, 1983 20. Ware, C. ,
Information Visualization: Perception to Design, Academic Press, 2000 |
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9. Corroborating
the content of the discipline with the expectations of the epistemic community,
professional associations and representative employers within the field of the
program
|
·
This course exists in the curriculum of many universities in the
world; ·
The results of course are considered by companies of software
particularly useful and topical. |
10.
Evaluation
|
Type
of activity |
10.1 Evaluation criteria |
10.2 Evaluation methods |
10.3 Share in the grade (%) |
|
10.4 Course |
-
know the basic elements and concepts of the Scientific Data Visualization; |
Written exam |
50% |
|
10.5 Seminar / Project |
-
complexity, importance and degree of timeliness of the synthesis made |
Paper presentation |
15% |
|
-
apply the course concepts -
problem solving |
Project presentation |
35% |
|
|
10.6
Minimum performance standards |
|||
|
Ø At least grade 5 at
written exam, paper presentations and project realised. |
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18 Oct. 2016
|
Cod |
Denumire |
Ore: C+S+L+P |
Finalizare |
Credite |
|
MID1035 |
Vizualizare şi validare în simulare |
2+1+0+1 |
E |
8 cr. |
|
MMC1016 |
Calcul paralel şi concurent |
2+1+0+1 |
E |
7 cr. |
|
MID1004 |
Modele formale în limbajele de programare |
2+1+0+1 |
E |
8 cr. |
|
MID1034 |
Metode de simulare |
2+1+0+1 |
E |
7 cr. |
|
TOTAL |
8+4+0+4=16 |
|
30 cr. |
|
|
Discipline facultative: |
||||
|
XND1203 |
Didactica domeniului şi dezvoltări în didactica specialităţii |
2+1+0+0 |
E |
5 cr. |
|
XND2204 |
Disciplină opţională (1) |
1+2+0+0 |
E |
5 cr. |
|
Code |
Subject |
||
|
MID1035 |
Visualization and Validation in Simulation |
||
|
Semester |
Hours: C+S+L+P |
Category |
Status |
|
4 |
2+1+0+1 |
speciality |
optional |
11
Oct. 2016