Visual Analytics of Temporal Event Sequences in News Streams

Krstajic, Milos

Visual Analytics of Temporal Event Sequences in News Streams

Files

Krstajic_0-263456.pdf(17.58 MB)

Date

2014

Publication type

Dissertation

Abstract

Finding new ways of extracting and analyzing useful information from exploding volumes of unstructured and semi-structured text sources has become one of the greatest challenges in the era of big data. After new technologies have enabled efficient solutions for collecting and storing these data, the next step in computer science research is to develop scalable approaches for efficient analysis of dynamics in text streams. This dissertation addresses this challenge by examining how visual analytics can help the users gain new insights from systems for explorative analysis of events in text streams that are more efficient and easier to use. My work revolves around the concept of streaming visual analytics, whose goal is to combine resource constraints of the computer and time constraints of the user to provide more scalable tools. I identify challenges in the user, data and visualization domain, discuss open issues and derive design considerations to help practitioners in developing future systems for incremental data. Based on this approach, I describe novel visual analytics methods for detection and exploration of events in news streams: CloudLines, a compact overview visualization for events in multiple event sequences in limited space, and Story Tracker, a visual analytics system for exploration of news story development and their complex properties. Novel experimental visualizations are introduced to demonstrate the applicability of the approach in real time monitoring scenarios. I describe how the streaming visualization concepts pervade my work and outline directions for future research.

Summary in another language

Eine der größten Herausforderungen des Big Data Zeitalters ist die Extraktion und Analyse von nützlichen Informationen aus großen und wachsenden Datenvolumina. Neu entwickelte Technologien ermöglichen effizientes Abrufen und Speichern von Textquellen. Die daraus nachfolgende Herausforderung für die Informatik besteht darin, skalierbare Methoden zur Analyse der Dynamik von Textdatenströmen zu entwickeln. Diese Dissertation bietet Lösungen zu diesen Herausforderungen und untersucht, wie Visual Analytics die Benutzer unterstützen kann, neue Einsichten mit Hilfe von Systemen zur explorativen Analyse von Ereignissen in Textströmen zu gewinnen, wobei diese effizient und intuitiv zu benutzen sind. Meine Arbeit stellt das Konzept der “Streaming Visual Analytics” vor, dessen Ziel es ist, ressourcenbedingte Einschränkungen des Computers und Zeitvorgaben von Benutzern zu verbinden, um skalierbarere Werkzeuge zur Analyse von Textströmen bereitzustellen. Dafür werden Herausforderungen in Hinsicht auf Benutzer, Daten und Visualisierungen für Analysewerkzeuge identifiziert sowie offene Fragestellungen in diesem Forschungsthema diskutiert. Des Weiteren stelle ich Gestaltungsprinzipien bereit, um Forschern bei der Entwicklung neuer Systeme zur inkrementellen Datenanalyse zu helfen. Basierend auf diesem Ansatz beschreibe ich neue Visual Analytics Methoden zur Erkennung und Exploration von Ereignissen in Nachrichtenströmen: CloudLines, eine kompakte Visualisierung von Ereignissen auf beschränktem Raum, die in mehrere parallele Sequenzen gegliedert werden, oder Story Tracker, ein Visual Analytics System zur Exploration der Entwicklung von Nachrichtengeschichten sowie ihrer komplexen Eigenschaften. Des Weiteren werden innovative Visualisierungen vorgestellt, mit deren Hilfe die Praktikabilität dieses Ansatzes in Echtzeit-Überwachungsszenarien demonstriert wird. Ich beschreibe, inwiefern Streaming Visualisierung diese Arbeit motiviert sowie strukturiert und schneide weitere Forschungsmöglichkeiten in diesem Bereich an.

Subject (DDC)

004 Computer Science

Keywords

Visual Analytics, Streaming Visualization, Visual Text Data Analysis, Information Visualization, Streaming Text Data, Real-time Text Analysis, Event Detection and Tracking, Topic Evolution

Cite This

ISO 690

KRSTAJIC, Milos, 2014. Visual Analytics of Temporal Event Sequences in News Streams [Dissertation]. Konstanz: University of Konstanz

BibTex

@phdthesis{Krstajic2014Visua-29355,
  year={2014},
  title={Visual Analytics of Temporal Event Sequences in News Streams},
  author={Krstajic, Milos},
  address={Konstanz},
  school={Universität Konstanz}
}

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Examination date of dissertation

June 18, 2014

University note

Konstanz, Univ., Doctoral dissertation, 2014