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:
Priv.-Doz. Dr.-Ing.habil. Dirk Joachim Lehmann
Dates:
Donnerstag, 09:00 bis 11:00, G29-335
Classification:
Vorlesung, 2 SWS, ECTS-Credits: 5

WPF CV;B 4-6
WPF IF;B 4-6
WPF IngIF;B 4-6
WPF WIF;B 4-6
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:
> Additional Information <

Prüfungstermine

Prüfungen finden in 243 Spezialgerätelabor statt

Mögliche Themen für die Hausarbeit:

Bitte wählt bis zum 09.11.2017 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 als PDF oder Word-Dokument bis spätestens 18.01.2018 elektronisch an dirk@isg.cs.uni-magdeburg.de

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

- Sandra Held, Clustering Approaches

- Iris-Maria Banciu, Graph-Visualization

- André Bloemer, Visualization Lies

- Majd Adawieh, Machine Learning vs. Data Mining

- Ulrike Peitsch, Text Visualisierung

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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[AM104] T. Salzbrunn and G. Scheuermann
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A Clustering-based Visualization Technique to Emphasize Meaningful Regions of Vector Fields
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[AM109] George Haller
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[AM111] Turkay, Cagatay and Filzmoser, Peter and Hauser, Helwig
Brushing Dimensions – A Dual Visual Analysis Model for High-Dimensional Data
IEEE Trans. Vis. Comput. Graph., 2011

[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
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