Interactive Visual Analysis of Process Data

Ove Daae Lampe

PHDTHESIS, Sep, 2011

• paper

Abstract

Data gathered from processes, or process data, contains many different aspects that a visualization system should also convey. Aspects such as, temporal coherence, spatial connectivity, streaming data, and the need for in-situ visualizations, which all come with their independent challenges. Additionally, as sensors get more affordable, and the benefits of measurements get clearer we are faced with a deluge of data, of which sizes are rapidly growing. With all the aspects that should be supported and the vast increase in the amount of data, the traditional techniques of dashboards showing the recent data becomes insufficient for practical use. In this thesis we investigate how to extend the traditional process visualization techniques by bringing the streaming process data into an interactive visual analysis setting. The augmentation of process visualization with interactivity enables the users to go beyond the mere observation, pose questions about observed phenomena and delve into the data to mine for the answers. Furthermore, this thesis investigates how to utilize frequency based, as opposed to item based, techniques to show such large amounts of data. By utilizing Kernel Density Estimates (KDE) we show how the display of streaming data benefit by the non-parametric automatic aggregation to interpret incoming data put in context to historic data.

Published

• ISBN: 978-82-308-1910-4
• School: Department of Informatics, University of Bergen, Norway
• Date: Sep 2011

Media

BibTeX

@phdthesis{lampe11thesis,
  title = {Interactive Visual Analysis of Process Data},
  author = {Ove Daae Lampe},
  year = {2011},
  abstract = {Data gathered from processes, or process data, contains many different aspects
that a visualization system should also convey. Aspects such as, temporal
coherence, spatial connectivity, streaming data, and the need for in-situ
visualizations, which all come with their independent challenges. Additionally,
as sensors get more affordable, and the benefits of measurements get clearer we
are faced with a deluge of data, of which sizes are rapidly growing. With all
the aspects that should be supported and the vast increase in the amount of
data, the traditional techniques of dashboards showing the recent data becomes
insufficient for practical use. In this thesis we investigate how to extend the traditional
process visualization techniques by bringing the streaming process data
into an interactive visual analysis setting. The augmentation of process visualization
with interactivity enables the users to go beyond the mere observation,
pose questions about observed phenomena and delve into the data to mine for
the answers. Furthermore, this thesis investigates how to utilize frequency based,
as opposed to item based, techniques to show such large amounts of data. By
utilizing Kernel Density Estimates (KDE) we show how the display of streaming
data benefit by the non-parametric automatic aggregation to interpret incoming
data put in context to historic data.},
  school = {Department of Informatics, University of Bergen, Norway},
  month = {Sep},
  ISBN = {978-82-308-1910-4},



}