Differenze

Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

--- magistraleinformaticaeconomia:va:start [18/02/2019 alle 13:41 (6 anni fa)] – [News] Salvatore Rinzivillo
+++ magistraleinformaticaeconomia:va:start [30/05/2025 alle 07:47 (6 settimane fa)] (versione attuale) – [Next Exams] Salvatore Rinzivillo
@@ Linea 2: / Linea 2: @@
   * **Salvatore Rinzivillo** ([[rinzivillo@isti.cnr.it]])
+==== Quick access links ====
+  * MS Teams Channel: https://teams.microsoft.com/l/team/19%3AySCF-pb5z22vNq2oSroKWz-ejSxNTyzEG0egi-BPrHo1%40thread.tacv2/conversations?groupId=4094af8b-f2f0-4d2f-a38f-72e2a8adb25e&tenantId=c7456b31-a220-47f5-be52-473828670aa1
+  * Telegram channel: https://t.me/+6PTkOzAWcWswMWI8
+  * All source code of exercises are available at the URL: https://github.com/va602aa-master
 ===== Schedule =====
-  * Monday, 16:00 - 18:00, Aula N1 Polo Fibonacci
+  * Wednesday, 11:00 - 13:00, Room M1
-  * Friday, 14:00 - 16:00, Aula V1 Polo Fibonacci
+  * Friday, 11:00 - 13:00, Room M1
 ===== News =====
-To keep updated with the last news of the course, subscribe at the Telegram channel: https://t.me/va602aa
+To keep updated with the last news of the course, subscribe at the Telegram channel: https://t.me/+6PTkOzAWcWswMWI8
-  * **[new]** The new edition is starting on Monday 18th February 2019 at 16:00 in Aula N1
+   * :!: **March 21** Today the class will start at 11:30 (instead of the usual 11:15)
+   * **February 19** The start of the course has been postponed due to the teacher being affected by the flu. Lessons will commence on February 26th.
 ===== Exams =====
 Students will be admitted to the exam after the registration on the website [[http://esami.unipi.it]].
-The exam consist of a discussion of the project. It is mandatory to submit a short report (6-10 pages) within the deadline by mail to instructor, specifying the tag "[VA]" in the subject.
+The exam consists of a discussion of the project. It is mandatory to submit a short report (6-10 pages) within the deadline by mail to the instructor, specifying the tag "[VA]" in the subject.
+Planned dates:
+  * Please log on to the portal for registration to get the next dates
 ==== Project assignment ====
-  * A project should have the following requirements:
-  * The application should contain several visual widgets, each providing insights on a selection of dimensions of the original data
-  * It is possible to use state-of-the-art charts (bar charts, line charts, etc.) and libraries (plotly, nvd3, etc). It is a plus to implement a novel, original visualization to present the data in a creative, non-trivial way. (see examples on Vast Challenge 2008 developed in class)
-  * Interactivity should be implemented, providing toolbars, selections and filters for the data.
-  * The visual widget should interact among them, realising a set of linked display to browse the data across multiple dimensions
-  * The project should be submitted as a Git repository
-  * The project report should be submitted 4 days before the discussion and should discuss at least the following points:
-    * Description of data and presentation of the pattern or model to communicate
-    * design choices: colors, interactions, shapes, transformations)
-    * state-of-art: similar tools or interfaces for the same problem
-    * detailed description of the visualization with description of interaction
-    * use case example for an analytical task
-The student may choose one of the following project proposals. She/he can also propose an additional topic. In this case a project proposal should be submitted for approval, containing a description of the data, a sketch of the possible visualization and the motivation for the project.
+The final project should have the following requirements:
+    * The application should contain several visual widgets, each providing insights on a selection of dimensions of the original data
+    * It is possible to use state-of-the-art charts (bar charts, line charts, etc.) and libraries (plot.ly, vega, etc).
+    * The final evaluation will take into account the implementation of a novel, original visualization to present the data in a creative, non-trivial way, using D3.js (see examples on Vast Challenge 2008 developed in class). You can refer to visualization techniques already present in the literature, by adapting or implementing part of the solution.
+    * Interactivity should be implemented, providing toolbars, selections, and filters for the data.
+    * The visual widget should interact among them, realizing a set of linked displays to browse the data across multiple dimensions
+    * The project should be submitted as a Git repository
+    * The project report should be submitted 4 days before the discussion and should discuss at least the following points:
+      * Description of data and presentation of the pattern or model to communicate
+      * design choices: colors, interactions, shapes, transformations)
+      * state-of-art: similar tools or interfaces for the same problem
+      * Detailed description of the visualization with a description of the interaction
+      * use case example for an analytical task
+The student may choose one of the following project proposals. She/he can also propose an additional topic. In this case, a project proposal should be submitted for approval, containing a description of the data, a sketch of the possible visualization, and the motivation for the project.
+=== Final Exam Project Assignment: VAST Challenge 2024 Datasets ===
+**Objective:**
+For your final exam, you will engage with synthetic yet realistic datasets from the VAST Challenge 2024. Your task is to develop a visual analytics solution that addresses specific research questions posed in one of the mini-challenges (MiniChallenge 2 or MiniChallenge 3).
+==== Overview: ====
+The datasets for this project are synthetic representations of network graph structures, where entities are depicted as nodes and events or relationships among these entities as edges. Your goal is to analyze these data to uncover insights by answering the research questions provided in the selected MiniChallenge.
+==== Assignment Details: ====
+**Dataset Selection:**
+   - Choose one of the following MiniChallenges:
+     - [MiniChallenge 2](https://vast-challenge.github.io/2024/MC2.html)
+     - [MiniChallenge 3](https://vast-challenge.github.io/2024/MC3.html)
+**Data Understanding:**
+   - Familiarize yourself with the synthetic dataset, understanding its structure, nodes (entities), and edges (relationships/events).
+   - Identify key characteristics of the network, such as node types, edge types, and any metadata provided.
+**Research Questions:**
+   - Review the research questions posed by the selected MiniChallenge.
+   - Your visual analytics solution should address all the questions posed by the challenge.
+**Visual Analytics Solution:**
+   - Design and implement a visual analytics system tailored to explore the data effectively in response to the chosen research question(s).
+   - Utilize appropriate visualization techniques (e.g., graph layouts, node-link diagrams) to represent the network structure clearly.
+   - Incorporate interactive elements to enable dynamic exploration of the dataset.
+   - You can preprocess, transform, and prepare the given data in the format that is most appropriate for your visual design
+**Analysis and Insights:**
+   - Conduct an analysis using your visual analytics solution to address the selected research question(s).
+   - Document any insights or patterns discovered during your exploration that relate directly to the research questions.
-=== VAST Challenge 2017 ===
-The project assignment for the exam consist in the realisation of a web application addressing data and mini challenges presented for the VAST challenge 2017 (http://www.vacommunity.org/VAST+Challenge+2017). The general contest of the challenge asks to analyse and explain the possible causes of pollutants spreading in a natural park, threatening the survival of a bird species in the park.
-== Rules ==
-   * It is possible to choose among Mini Challenge 1 and Mini Challenge 2: The first mini challenge regards the analysis of logs of traffic flows of vehicles within the park; the second mini challenge ask to analyse the data of emissions of industries and company in the neighbourhood of the park
-   * The project can be developed also in group (at most two students). For the groups, at least the two challenges should be addressed.
-   * The data can be downloaded from the website above
-=== Didactic Data Mining ===
-This is a project that requires to implement a module with visual interface to explore and manage the project __Didactic Data Mining__ developed within the course of Data Mining.
-The module is implemented in Python and provides a RESTful interface to create an experiment, to insert a dataset and to follow the evolution of a data mining algorithm on the dataset.
-== Rules ==
-     * The students should select a data mining algorithm after a preliminary discussion with the project manager (Prof Monreale)
-     * The requirements of the project are discussed in this extended committe. The student is autonoums in developing and proposing the visual interface
-     * From a technical point of view, a few constrains are already set:
-       * The project should be developed within the GitHub platform, accessing the repository of the main Project (it will be created a branch dedicated to the student)
-       * The interfaces and data schema of the whole project are fixed and cannot be changed (any modification should be discussed)
-       * The module developed by the student should conform the code quality rules already set (linting, testing, etc.)
-       * The project uses the Vue.js framework for developing the application
-=== Network Diffusion Library ===
-This is a project that requires to extend the visual interface of the __NDLib - Network Diffusion Library__ developed within the KDDLab.
-The core library is implemented in Python and provides a RESTful interface to create an experiment, to insert a network and to execute a diffusion simulation over the network.
-== Rules ==
-  * The students should select one task to extent the interface, after a preliminary discussion with the project managers (Rossetti, Milli, Rinzivillo)
-  * The requirements of the project are discussed in this extended committe. The student is autonoums in developing and proposing the visual interface
-  * From a technical point of view, a few constrains are already set:
-    * The project should be developed within the GitHub platform, accessing the repository of the main Project (it will be created a branch dedicated to the student)
-    * The interfaces and data schema of the whole project are fixed and cannot be changed (any modification should be discussed)
-    * The module developed by the student should conform the code quality rules already set (linting, testing, etc.)
-    * The project uses the Vue.js framework for developing the application
 ==== Next Exams ====
-  * **2018-09-10**: Deadline to submit report and repository of the code is 2018-09-06
+  * June 9th (submit your project and report by June 5th)
-  * <del>**2018-07-09**: Deadline to submit report and repository of the code is 2018-07-05</del>
+  * June 30th (submit your project and report by June 26th)
-  * <del>**2018-06-18**: Deadline to submit report and repository of the code is 2018-06-14</del>
+  * July 22th (submit your project and report by July 17th)
 ===== Textbooks =====
+  * Visual Analytics for Data Scientists. Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay, Stefan Wrobel. Springer, 2020. ISBN: 978-3-030-56146-8
   * [[http://www.vismaster.eu/news/mastering-the-information-age/|VisMaster - Mastering the information age]]
-  * Processing: a programming handbook for visual designers and artists. Casey Reas, Ben Fry. MIT Press, 2007
   * Design for Information. Isabel Meirelles, Rockport Publisher,2013.
   * Interactive Data Visualization for the Web, Scott Murray, O'Reilly Atlas, 2013
+  *
 ===== Useful Resources =====
   * Tools
-    * [[http://www.openprocessing.org/classroom/4698| OpenProcessing Classroom]]
-    * [[http://processing.org|Processing.org]]
     * [[http://d3js.org|D3 Javascript Library]]
-    * [[http://piktochart.com/|PiktoChart]]
+    * [[https://vuejs.org/| Vue.js Framework]]
-    * [[http://jsbin.com/|JS Bin]]
+    * [[https://nodejs.org/| Node.js]]
   * Reading Material
     * [[http://www.slideshare.net/AmandaMakulec/data-visualization-resource-guide-september-2014|Data Visualization Resources (on Slideshare)]]
@@ Linea 90: / Linea 109: @@
     * [[http://www.informationisbeautiful.net/]]
     * [[http://visualoop.com/]]
-    * [[http://www.openprocessing.org/]]
-    * [[https://www.flickr.com/groups/processing/|Set of images of Processing artwork on Flickr]]
-  * Processing libraries
-    * [[http://www.sojamo.de/libraries/controlP5/|Extends sketches with toolbar]]
-    * [[http://toxiclibs.org/|Utilities]]
-    * [[http://unfoldingmaps.org/|Mapping library]]
-==== Other resources ====
-  * [[http://dati.toscana.it|Open Data Tuscany Region]]
-  * [[http://riccomini.name/posts/game-time-baby/|Sport results]]
-  * [[http://yahoolabs.tumblr.com/post/89783581601/one-hundred-million-creative-commons-flickr-images|Flickr Images dataset]]
-  * [[http://www.yelp.com/dataset_challenge]]
-  * [[http://socialcomputing.asu.edu/pages/datasets]]
-  * [[http://networkrepository.com/]]
-  * [[http://chriswhong.com/open-data/foil_nyc_taxi/]]
-  * [[http://www.sociopatterns.org/datasets/]]
-  * [[http://konect.uni-koblenz.de/]]
-  * [[http://snap.stanford.edu/data/|Stanford Large Network Dataset Collection]]
+{{ :magistraleinformaticaeconomia:va:2025:va_lesson16_storytelling.pdf |}}
 ===== Class Calendar =====
-All exercizes and code discussed during each lesson are available as a Git repository at:
+All exercises and code discussed during each lesson are available as a Git repository at:
-https://github.com/rinziv/VA2017
+https://github.com/va602aa-master
-| ^ Day ^ Aula ^ Topic ^ Learning material ^
-^1.| 19.02.2018 14:00-16:00 | N1 | Intro: Visual Analytics Process; | {{ :magistraleinformaticaeconomia:va:2018:va_lesson1_introcourse.pdf |Slides}} ; VisMaster Book (Chapter 2) |
-|  | 23.02.2018 14:00-16:00 | -- | No lesson today |  |
-^2.| 26.02.2018 14:00-16:00 | N1 | Intro: Visual Analytics Process; | {{ :magistraleinformaticaeconomia:va:2018:va_lesson2_vision_perception.pdf |Slides}}  |
-^3.| 02.03.2018 14:00-16:00 | C1 | Development Environment setup: Node.js, NPM, GIT | {{ :magistraleinformaticaeconomia:va:2018:va_lesson3_nodejs_npm_git.pdf |Slides}}  |
-^4.| 06.03.2018 11:00-13:00 | N1 | Visual Variables | {{ :magistraleinformaticaeconomia:va:2018:va_lesson4_visualvariables.pdf |Slides}}  |
-|  | 09.03.2018 14:00-16:00 | -- | No lesson today |  |
-^5.| 12.03.2018 14:00-16:00 | N1 | Introduction to HTML, CSS, Javascript | {{ :magistraleinformaticaeconomia:va:2018:va_lesson5_html_css_js.pdf |Slides}}  |
-^6.| 13.03.2018 11:00-13:00 | L1 | Introduction to SVG | {{ :magistraleinformaticaeconomia:va:2018:va_lesson6_svg.pdf |Slides}}  |
-|  | 16.03.2018 14:00-16:00 | -- | No lesson today |  |
-^7.| 19.03.2018 14:00-16:00 | N1 | Introduction to D3.js | {{ :magistraleinformaticaeconomia:va:2018:va_lesson7_d3js_intro.pdf |Slides}}<html><br/></html> {{https://github.com/VA602AA-master/First-Project |GitHub - First project}} <html><br/></html>{{https://github.com/VA602AA-master/first-d3|GitHub First-D3}} |
-^8.| 23.03.2018 14:00-16:00 | C1 | Do and Donts. Examples and case studies | {{ :magistraleinformaticaeconomia:va:2018:va_lesson8_doanddonts.pdf |Slides}} |
-^9.| 26.03.2018 14:00-16:00 | N1 | First examples with D3 | {{https://github.com/VA602AA-master/lines|GitHub Lines}} |
-|  | Spring break           |    | No lessons - Assignment: implement //lines// project with other shapes|
-^10.| 12.04.2018 14:00-16:00 | O | Project assignement; Color Models | {{ :magistraleinformaticaeconomia:va:2018:va_lesson9_colors.pdf |Slides}} |
-^11.| 17.04.2018 16:00-18:00 | L1 | Project assignement; Scales | Slides |
-^12.| 20.04.2018 14:00-16:00 | C1 | Scales and Reusable Chart Components | {{ :magistraleinformaticaeconomia:va:2018:va_lesson11_reusable_modules.pdf |Slides}} <html><br/></html> {{https://github.com/VA602AA-master/lines|GitHub Lines}}<html><br/></html>{{https://bost.ocks.org/mike/chart/| Resusable Charts in D3}} |
-^13.| 23.04.2018 14:00-16:00 | L1 | Data Handling - Crossfilter | {{ :magistraleinformaticaeconomia:va:2018:va_lesson12_data_handling.pdf |Slides}} |
-^14.| 27.04.2018 14:00-16:00 | C1 | Data Handling - Express and DBMS | see Lesson 13  |
-===== GITHub repository =====
-All source code of exercises are available at the URL: https://github.com/VA602AA-master
+A collection of Observable Notebooks are available at https://observablehq.com/collection/@rinziv/va602aa
+Recordings of lessons on Microsoft Teams are accessible within the channel of the course.
+| ^ Day ^ Topic ^ Learning material ^
+^ 01| 2025/02/26 | Intro: Visual Analytics Process; |{{ :magistraleinformaticaeconomia:va:2024:va_lesson1_introcourse.pdf |Slides}} ; VisMaster Book (Chapter 2) |
+^ 02| 2025/02/28 | Vision and Cognition; |{{ :magistraleinformaticaeconomia:va:2025:va_lesson2_vision_perception.pdf | Slides}} |
+^ 03| 2025/03/05 | Visual Variables; | {{ :magistraleinformaticaeconomia:va:2025:va_slide_03_visual-variables-slides.pdf | Slides}} |
+^ 04| 2025/03/07 | Introduction to Python Altair Library | {{ :magistraleinformaticaeconomia:va:2025:va_slide_04_vegaaltair.pdf | Slides}}; {{https://altair-viz.github.io/index.html| website}} |
+^ 05| 2025/03/12 | Color Models | {{ :magistraleinformaticaeconomia:va:2025:va_lesson5_colors.pdf | Slides}} |
+^ | 2025/03/14 | No lesson | --  |
+^ 06| 2025/03/19 | Toolbox: HTML, CSS, JS | {{ :magistraleinformaticaeconomia:va:2025:va_lesson6_html_css_js.pdf | Slides}} |
+^ 07| 2025/03/21 | NPM, GIT and Vue.js | {{ :magistraleinformaticaeconomia:va:2025:va_lesson7_nodejs_npm_git.pdf | Slides}} |
+^ | 2025/03/26 | No lesson | --  |
+^ | 2025/03/28 | No lesson | --  |
+^ 08| 2025/04/02 | Chart Taxonomy | {{ :magistraleinformaticaeconomia:va:2025:va_lesson8_charting_taxonomy.pdf | Slides}} |
+^ 09| 2025/04/04 | Intro to D3.js |  |
+^ | 2025/04/09 | No lesson | --  |
+^ | 2025/04/11 | No lesson | --  |
+^ 10| 2025/04/16 | Scale functions | {{ :magistraleinformaticaeconomia:va:2025:va_lesson10_scales.pdf | Slides}}; {{https://observablehq.com/@d3/introduction-to-d3s-scales | Notebook}}  |
+^ 11| 2025/04/23 | Question and answering | --  |
+^ | 2025/04/30 | Scale functions (cont.d) | {{ :magistraleinformaticaeconomia:va:2025:va_lesson10_scales.pdf | Slides}}; {{https://observablehq.com/@d3/quantile-quantize-and-threshold-scales | Notebook}}  |
+^ 12| 2025/05/07 | Hierachical Data | {{ :magistraleinformaticaeconomia:va:2025:va_lesson12_hierarchies.pdf | Slides}}  |
+^ 13| 2025/05/09 | Modular Programming in D3 and Javascript| {{https://observablehq.com/d/f9a177396c890d21| Notebook}} |
+^ 14| 2025/05/14 | Geographic Data | {{ :magistraleinformaticaeconomia:va:2025:va_lesson14_geography.pdf | Slides}}  |
+^ 15| 2025/05/16 | Geography in D3  | {{https://observablehq.com/@rinziv/geographic-data| Notebook}}  |
+^ 16| 2025/05/21 | Visual Storytelling | {{ :magistraleinformaticaeconomia:va:2025:va_lesson16_storytelling.pdf | Slides}}  |
+^ 17| 2025/05/23 | VAST 2008 - Project  | {{https://github.com/VA602AA-master/VC2018 | GitHUB Repository}}  |
+^ 18| 2025/05/28 | VAST 2008 - Project (cont.d) |  |
+^ 19| 2025/05/30 | VAST 2008 - Project (cont.d) |  |
 ===== Previous Editions =====
+  * [[magistraleinformaticaeconomia:va:Course2024]]
+  * [[magistraleinformaticaeconomia:va:Course2023]]
+  * [[magistraleinformaticaeconomia:va:Course2022]]
+  * [[magistraleinformaticaeconomia:va:Course2021]]
+  * [[magistraleinformaticaeconomia:va:Course2020]]
+  * [[magistraleinformaticaeconomia:va:Course2019]]
   * [[magistraleinformaticaeconomia:va:Course2018]]
   * [[magistraleinformaticaeconomia:va:Course2017]]