Daten, Visualisierung und Visual Analytics
Big Data ist ein Schlagwort unserer Zeit, denn es entstehen Daten fast bei jedem Prozess, ob in Wirtschaft, Wissenschaft, oder im sozialen Bereich.

In den letzten Jahren ist es immer einfacher geworden solche Daten persistent zu sammeln, aufgrund moderner Speichertechnologien. Deren Auswertung hat sich jedoch zunehmend als schwierig erwiesen.

Zum einen fehlt es oft an geeigneten Methoden, zum anderen hat sich gezeigt, das Daten auch innerhalb ihrer Eigenschaften bzgl.
ihrer zugrunde liegenden Domain interpretiert werden müssen. Nur so kann sichergestellt werden, dass eine Datenauswertung in relevante
Handlungs- und Entscheidungskompetenz Überführt wird. Auf der anderen Seite führt die schiere Menge an verfügbaren Daten
ebenfalls zu erschwerten Auswertungsbedingungen, weil weder die Daten in ihrer Gesamtheit gesichtet werden können noch eine geeignete Strategie
die Daten in unabhängige Sub-Datenmengen zu zerlegen trivial benannt werden kann.

In dieser Vorlesung diskutieren wir Methoden Daten automatisch zu analysieren, ebenso wie Methoden den Nutzer von Daten in den Analyseprozess mit einzubinden,
welches unter dem Stichwort Daten Visualisierung durchaus dem ein oder anderen unter Ihnen bekannt sein dürfte. Eine logische Konsequenz ist es beide Ansätze
miteinander zu kombinieren und um interaktive Methoden zu bereichern: Ein Ansatz welcher als Visual Analytics bezeichnet wird und welchen wir ebenfalls in dieser
Veranstaltung aufgreifen und diskutieren werden.

Die Veranstaltung selbst ist breit aufgestellt und self-contained. Notwendiges Wissen für das Verständnis von relevanten Methoden wird in den ersten Wochen vorgestellt, um im folgenden Automatische Analyse Methoden, visuelle Methoden, und Methoden der Visual Analytics
zu besprechen.
Lecturer:
Dr.-Ing. Dirk Joachim Lehmann
Dates:
Montag, 13:00 bis 15:00, G29-335, 1. Termin: 12.10.2015
Classification:
Vorlesung, ECTS-Credits: 5

Computervisualistik (82152) Bachelor 4 - 6 WPF
Informatik (82150) Bachelor 4 - 6 WPF
Ingenieurinformatik (82157) Bachelor 4 - 6 WPF
Wirtschaftsinformatik (82159) Bachelor 4 - 6 WPF
Completion:
Seminararbeit + Schriftliche Prüfung (2 h am Ende des Semesters) (bei einer Teilnehmerzahl weniger als 15 findet eine mündliche Prüfung statt)
Certificate/Schein:
bestandene Seminararbeit + Prüfung
Additional Information:
> Lecture website <

Lehrmaterial

Introduction

Part_I:Basics_in_Math_and_Data_Categories_1

Part_I:Basics_in_Math_and_Data_Categories_2

Part_II_AutomaticApproaches_1

Part_II_AutomaticApproaches_2

Part_II_AutomaticApproaches_3

Part_III_VisualizationApproaches_1

Part_III_VisualizationApproaches_2

Part_III_VisualizationApproaches_3

Part_III_VisualizationApproaches_4

Part_IV_VisualAnalytics_1

Prüfungstermine

Prüfungen finden in 243 Spezialgerätelabor statt

Mögliche Themen für die Hausarbeit:

Bitte wählt bis zum 30. 10. 2015 ein Thema für eure Hausarbeit und sendet es mir per Mail zu (dirk@isg.cs.uni-magdeburg.de). Danke.
Hinweis: Nur Einzelarbeiten sind zulässig. Themen werden nicht doppelt vergeben (first come, first serve).

- Graph-Visualization (z.B. Edge-Bundles, Tree-Maps etc.)

- Clustering Approaches (z.B. K-Means, Local Outlier Factor, DBScan, Clusterfactors)

- Machine Learning vs. Data Mining

- Mathematische Grundlagen für die Visualisierung (Projektionen, Abbildungen, Einbettungen, Transformationen, Matrizen, Statistik, Optimierung, etc.)

- Bivariate vs. Multivariate Visualization Approaches (z.B. Scatterplots, Parallel Coordinates, RadVis)

- Globale vs. Lokale Projektionen (z.B. Lamp)

- Approaches for Focus and Context and Level of Detail in Visual Analytics

- Quality Metrics in Visual Search

- Generation of high-dimensional or multivariate synthetic Data

- Text Visualization

- Methoden der Rück-Projektion von Merkmalen im Visualisierungsraum auf Strukturen im Datenraum (z.B. iLamp)

- Merkmalsdetektion in Visualisierungen (Features-Spaces, Ridges, Density Peaks, allgemein Topologie, etc.)

- Data Touren hochdimensionaler Daten (Grand Tour, PCA Tour, Optimal Set of Projections, Projection Pursuit etc.)

- Sub-Space Clustering

- Explorative Datenvisualisierung vs. visuelle Representation von Daten

- Visualisierung von Medizinischen Daten ODER Biologischen Daten ODER Strömungsdaten

- Visualization Lies: Fehler in der Analyse von Daten hervorgerufen durch die Visualisierung von Daten

- Visual Design: Methoden zur Wahl geeigneter Visualisierungen

- …

- Eigenes Thema

Hausarbeitsthemen (Abgabe der Hausarbeit bis spätestens 23.1.2016 elektronisch an dirk@isg.cs.uni-magdeburg.de):

Sobald ihr eure Themen gewählt habt, werden diese hier dargestellt, um Dopplungen zu vermeiden.

- Felix Sturm, Merkmalsdetektion in Visualisierungen

- Eric Nordmann, Visualisierung von Medizinischen Daten

- Julia Pfeffer , Visualisierung von Biologischen Daten

- Alexander Seelig, Textvisualisierung

- Stephan Fensky , Clustering Approaches

- Daniel Sopauschke , Visualisierung von Strömungsdaten

- Anne Döbler, Machine Learning vs. Data Mining

- Gabriel Moczalla, Graph Visualization

- Hannes Martinke, Bivariate vs. Multivariate Visualization Approaches

- Jakob Starick, Visualization Lies

- Stefan Müller, Explorative Datenvisualisierung vs. visuelle Representation von Daten

- Franziska Heyden, Visual Design: Methoden zur Wahl geeigneter Visualisierungen

- Kay Wieczorek, Approaches for Focus and Context and Level of Detail in Visual Analytics

- Andrea Salmon, Mathematische Grundlagen für die Visualisierung

- Tamara Rautenstengel, Generation of high-dimensional or multivariate synthetic Data

Hinweise zum Schreiben wissenschaftlicher Arbeiten:

Wenn möglich, sollte für die Erstellung von wiss. Arbeiten ein Textsatzsystem eingesetzt werden.
Im Gegensatz zu klassischen WYSIWYG Systemen wie MS Word u.ä., folgen solche Textsatzsysteme
dem WYSIWYM-Prinzip (What You See Is What You Mean). Der Umstieg zwischen den Systemen bedarf
einer gewissen Eingewöhnungsphase, er lohnt sich dennoch. Gewinnt man doch eine große Menge an
Freiheiten bei der Text, Formel und Bildgestaltung; und auch komplexe Dokumente lassen sich effizient erstellen und einfach verwalten.

Das wohl bekannteste professionelle Textsatzsystem ist LaTeX (ursprünglich TeX).
Ein geeigneter kostenloser Editor für Tex ist MikTex. Zur Verwaltung von Referenzen kann JabRef eingesetzt und genutzt werden.

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IEEE Trans. on Visualization and Computer Graphics, 2013

[AM113] E. Kandogan
Star Coordinates: A Multi-Dimensional Visualization Technique with Uniform Treatment of Dimensions
Proc. of the IEEE Information Visualization Symposium, 2000

[AM114] Dirk J. Lehmann and Holger Theisel
Orthographic Star Coordinates
IEEE Trans. on Visualization and Computer Graphics (Proc. IEEE Information Visualization), 2013

[AM115] Alexander Klippel, Frank Hardisty, Chris Weaver
Star Plots: How Shape Characteristics Influence Classification Tasks
Cartography and Geographic Information Science, 2009

[AM116]Theisel, Holger
Higher Order Parallel Coordinates
VMV, 2000

[AM117] Dirk J. Lehmann and Holger Theisel
Optimal Sets of Projections of High-Dimensional
IEEE Transactions on Visualization & Computer Graphics (Proc. IEEE Information Visualization), 2015