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Introduction Visual Analytics VU (706.720) SS 2020 Vedran Sabol 1 , - PowerPoint PPT Presentation

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Introduction Visual Analytics VU (706.720) SS 2020 Vedran Sabol 1 , Eduardo Veas 2 , Tobias Schreck 3 1 Know-Center GmbH 2,1 Institute of Interactive Systems and Data Science, TU Graz 3 Institute of Computer Graphics and Knowledge Visualization, TU

  1. Introduction Visual Analytics VU (706.720) SS 2020 Vedran Sabol 1 , Eduardo Veas 2 , Tobias Schreck 3 1 Know-Center GmbH 2,1 Institute of Interactive Systems and Data Science, TU Graz 3 Institute of Computer Graphics and Knowledge Visualization, TU Graz March 3 rd 2020 March 3 rd , 2020 Vedran Sabol , Eduardo Veas, Tobias Schreck Visual Analytics VU - Introduction

  2. Structure of the Presentation • Organisational information  Theoretical part: 5 lectures (in March 2020)  Practical part: 3 mandatory presentations + 4 optional progress reviews  4 student deliverables • Goals and topics of the course • Examples • Course structure and calendar • Student presentations • Examination and grading March 3 rd , 2020 Visual Analytics VU - Introduction 2 Vedran Sabol , Eduardo Veas, Tobias Schreck

  3. Course Overview March 3 rd , 2020 Visual Analytics VU - Introduction 3 Vedran Sabol , Eduardo Veas, Tobias Schreck

  4. Course • Visual Analytics VU 706.720 (3.0 SSt, 5 ECTS credits) • Elective (optional) course for  Computer Science  Software Engineering and Management  Doctoral Studies • Catalogues: Knowledge Technologies, Multimedia Information Systems, Web and Data Science March 3 rd , 2020 Visual Analytics VU - Introduction 4 Vedran Sabol , Eduardo Veas, Tobias Schreck

  5. Lecturer Name: Vedran Sabol Know-Center Affiliation: Inffeldgasse 13, 5 th floor Office: Office hours: by appointment +43 316 873 30850 Phone: Email: vsabol@know-center.at March 3 rd , 2020 Visual Analytics VU - Introduction 5 Vedran Sabol , Eduardo Veas, Tobias Schreck

  6. Lecturer Name: Eduardo Veas Know-Center, TUG/ISDS Affiliation: Petersstrasse 116, EG Office: Office hours: by appointment +43 316 873 30858 Phone: Email: eveas@know-center.at March 3 rd , 2020 Visual Analytics VU - Introduction 6 Vedran Sabol , Eduardo Veas, Tobias Schreck

  7. Lecturer Name: Tobias Schreck TUG/CGV Affiliation: Inffeldgasse 16c Office: Office hours: by appointment +43 316 873 5403 Phone: Email: tobias.schreck@cgv.tugraz.at March 3 rd , 2020 Visual Analytics VU - Introduction 7 Vedran Sabol , Eduardo Veas, Tobias Schreck

  8. Language • Master course: lectures in English • Communication in German/English • If in German: please informally (Du)! • Project: English • Presentation: German/English March 3 rd , 2020 Visual Analytics VU - Introduction 8 Vedran Sabol , Eduardo Veas, Tobias Schreck

  9. Organization • Lectures  When: Tuesday, 10:00 – 12:00  Where: HS i9 (with exceptions, see time plan) • Registration for the course in TUG Online until 20.03.2020 23:59 • Course organised in 2 blocks 1. Theoretical part: 5 lectures (in March)  Presence at the theoretical lectures is highly recommended • but not obligatory 2. Practical part  Content: design and implementation of the visual analytics prototype  Presence at 3 student presentations IS OBLIGATORY for all students  Presence at 4 progress reviews is optional (but recommended) • Attendance at progress reviews requires notification to lecturers per email! March 3 rd , 2020 Visual Analytics VU - Introduction 9 Vedran Sabol , Eduardo Veas, Tobias Schreck

  10. Structure of the Course • Theoretical part: lectures  Topics directly related to the projects  Main results • acquisition of knowledge necessary for the practical part • D1 - Literature summary paper (each student works separately) • Practical part: design and implementation of a demo (in teams of 4)  Main results • D2 - Design of the VA user interface (in teams) • D3 - Review of another team’s design (in teams ) • D4 - demo implementation (in teams) • 3 student presentations (corresponding to deliverables D2, D3, and D4) – Including questions and discussion with the lecturers March 3 rd , 2020 Visual Analytics VU - Introduction 10 Vedran Sabol , Eduardo Veas, Tobias Schreck

  11. Structure of the Course 5 theoretical lectures: 1. Intro + Visual perception and visual encoding 2. Visualisation of multi-dimensional time series data 3. Visualisation of text corpora (incl. intro to data analytics) 4. Visual search and guided analytics 5. Visual exploration of (social-semantic) networks + Visualization using immersive technologies 5 topical areas for the practicals (in bold, above): • Scientific literature provided for each topical area  introduced in the corresponding theoretical lecture  necessary for writing a paper summary  useful for designing your visual analytics prototype March 3 rd , 2020 Visual Analytics VU - Introduction 11 Vedran Sabol , Eduardo Veas, Tobias Schreck

  12. Deliverable Submission Submission of 4 deliverables by the students 1. D1: Literature summary paper 2. D2: Design of the visual analytics interface 3. D3: Review of interface design of another student team 4. D4: Demo implementation • All submissions via TeachCenter:  D1 submitted by each student separately  D2, D3 and D4 submitted by teams (4 students per team) March 3 rd , 2020 Visual Analytics VU - Introduction 12 Vedran Sabol , Eduardo Veas, Tobias Schreck

  13. Student Presentations 3 presentations held by the students: 1. Design of the visual analytics interface 2. Review of interface design of another student team 3. Presentation and live demo of the implemented prototype • All teams members must attend the presentations  and all team members must talk and present  if someone cannot attend due to a valid reason, e.g. sickness, we will try to find another appointment March 3 rd , 2020 Visual Analytics VU - Introduction 13 Vedran Sabol , Eduardo Veas, Tobias Schreck

  14. Materials and Infrastructure • TeachCenter: https://tc.tugraz.at/main/mod/folder/view.php?id=15064  Lecture slides  Scientific literature for the 5 topical areas  File and data exchange  Team Registration  Presentation slot reservation  Student submissions • Course Homepage: http://kti.tugraz.at/staff/vsabol/courses/va  Course description  Links to lecture slides and external resources  Project overview Contents (TeachCenter , Homepage) to be refreshed by 15.03.2020!  will be extended over the course of the lecture March 3 rd , 2020 Visual Analytics VU - Introduction 14 Vedran Sabol , Eduardo Veas, Tobias Schreck

  15. Materials and Infrastructure • Lecture slides  https://tc.tugraz.at/main/mod/folder/view.php?id=15064  links also available on the lecture homepage • Literature repository  papers for students to read and summarize  separate for each of the 5 topical areas • introduced in the corresponding theoretical lectures  available on the TeachCenter • Newsgroup: tu-graz.lv.va • News server: news.tu-graz.ac.at • Newsgroup is the preferred way of communication for this course • There is no tutor, your questions will be answered by the lecturers March 3 rd , 2020 Visual Analytics VU - Introduction 15 Vedran Sabol , Eduardo Veas, Tobias Schreck

  16. Course Content March 3 rd , 2020 Visual Analytics VU - Introduction 16 Vedran Sabol , Eduardo Veas, Tobias Schreck

  17. Motivation • Creation of huge amounts of data  Unstructured and semi-structured data: text, images etc. • news, enterprise documentation, scientific publications, patents etc. • resources described by rich metadata  Network data • Highly structured: hypertext, social networks, semantic knowledge bases  Time series data • sensor measurements, logs, event series, health histories  Multi-dimensional data sets • tabular data sets, large number of columns (dimensions)  Visual Search and Guidance • Visual specification of data patterns for search and comparison • Intelligent guidance of users for interactive exploration March 3 rd , 2020 Visual Analytics VU - Introduction 17 Vedran Sabol , Eduardo Veas, Tobias Schreck

  18. Goals of the course • Goal : learn basics on automated data analysis  Short intro to Knowledge Discovery Process  Processing chain involving: selection, preprocessing, transformation, mining and interpretation of data • Goal : learn how to Involve humans in the analytical process  Visual Analytics  Use visualisation to support analysis of complex data  Combining visual and automatic analysis methods • Goal : learn how to apply Visual Analytics methods  on different types of data  in selected scenarios  using Web and immersive technologies March 3 rd , 2020 Visual Analytics VU - Introduction 18 Vedran Sabol , Eduardo Veas, Tobias Schreck

  19. Goals of the course • Goal : learn about methods for understanding complex data  Document repositories and search results  Graph data: social networks and semantic knowledge bases  Sensor and event data collected by mobile devices  Multidimensional data: data elements described by many different features • Goal: learn about presenting data and content with visual means  In an suitable, easy to understand way  Visual search and user guidance  Using Web technologies (primarily HTML5) and immersive technologies • Goal : comprehend data as an object of interactive analysis  Knowledge Discovery basics (also known as data mining): algorithmic analysis  Visual techniques for representing specific data types  Visual Analytics : application of algorithmic and visual methods for interactive data analysis March 3 rd , 2020 Visual Analytics VU - Introduction 19 Vedran Sabol , Eduardo Veas, Tobias Schreck

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