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 m?ssen. Nur so kann sichergestellt werden, dass eine Datenauswertung in relevante
Handlungs- und Entscheidungskompetenz ?berf?hrt wird. Auf der anderen Seite f?hrt die schiere Menge an verf?gbaren Daten
ebenfalls zu erschwerten Auswertungsbedingungen, weil weder die Daten in ihrer Gesamtheit gesichtet werden k?nnen noch eine geeignete Strategie
die Daten in unabh?ngige 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 d?rfte. Eine logische Konsequenz ist es beide Ans?tze
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 f?r das Verst?ndnis 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:
Wann & Wo:
Do. 11:00 bis 13:00 w?chentlich G29-335
(ab 16.10. 2014 w?chentlich)
Credits: 5
Classification:
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 Pr?fung (2 h am Ende des Semesters)
Certificate/Schein:
bestandene Seminararbeit + Pr?fung
Additional Information:
> Lecture website <

M?gliche Themen f?r die Hausarbeit:

Bitte w?hlt bis zum 30. 10. 2014 ein Thema f?r eure Hausarbeit und sendet es mir zu. Danke.

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

- Clustering Approaches (z.B. K-Means, Local Outlier Factor, Sub-space Clustering)

- Machine Learning vs. Data Mining

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

- 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 R?ck-Projektion von Merkmalen im Visualisierungsraum auf Struckturen im Datenraum (z.B. iLamp)

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

- …

- Own Topic

Kursmaterial & Slides:

Aus aktuellem Anlass werden Kursmaterialien nicht mehr per Netzwerk zur Verf?gung gestellt.
Kursmaterialien k?nnen bei mir abgeholt werden. Bitte vereinbart daf?r vorher elektronisch einen Termin mit mir
unter dirk@isg.cs.uni-magdeburg.de und bringt zu dem Termin bitte einen USB-Stick mit.

Hausarbeitsthemen:

Simone Bexten : Machine Learning vs. Data Mining
Martin Weise : Clustering Approaches
Tatsiana Zhyhalava : Bivariate vs. Multivariate Visualization Approaches
Hannes Seibt : Graph-Visualization
Dietrich Trepnau : Quality Metrics in Visual Search
Philipp : Medizinische Visualisierung
Stephan Fensky : Merkmalsdetektion in Visualisierungen

(elektronisch Abgabe: sp?testens zum 15 Januar 2015)

Hinweise zum Schreiben wissenschaftlicher Arbeiten:

Wenn m?glich, sollte f?r die Erstellung von wiss. Arbeiten ein Textsatzsystem eingesetzt werden.
Im Gegensatz zu klassischen WYSIWYG Systemen wie MS Word u.?., folgen solche Textsatzsysteme
dem WYSIWYM-Prinzip (What You See Is What You Mean). Der Umstieg zwischen den Systemen bedarf
einer gewissen Eingew?hnungsphase, er lohnt sich dennoch. Gewinnt man doch eine gro?e 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 (urspr?nglich TeX).
Ein geeigneter kostenloser Editor f?r Tex ist MikTex. Zur Verwaltung von Referenzen kann JabRef eingesetzt und genutzt werden.

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[AM103] D. J. Lehmann and H. Theisel
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[AM104] T. Salzbrunn and G. Scheuermann
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[AM111] Turkay, Cagatay and Filzmoser, Peter and Hauser, Helwig
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[AM112] Michael Sedlmair and Tamara Munzner and Melanie Tory
Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices
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
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[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

Recent news:
17.11.2017

Research Seminar Announcement

In the course of the Visual Computing research seminar Prof. Dr. Mario Botsch from the University of Bielefeld will give a talk with subsequent discussion about the topic ICSPACE: Motor Learning in Virtual Reality.

We would like to invite everyone interested to join us. The research seminar will take place on Friday 24.11.2017, 13:15 in G29-R335.

To the overview…