liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Efficient Information Visualization of Multivariate and Time-Varying Data
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Data can be found everywhere, for example in the form of price, size, weight and colour of all products sold by a company, or as time series of daily observations of temperature, precipitation, wind and visibility from thousands of stations. Due to their size and complexity it is intrinsically hard to form a global overview and understanding of them. Information visualization aims at overcoming these difficulties by transforming data into representations that can be more easily interpreted.

This thesis presents work on the development of methods to enable efficient information visualization of multivariate and time-varying data sets by conveying information in a clear and interpretable way, and in a reasonable time. The work presented is primarily based on a popular multivariate visualization technique called parallel coordinates but many of the methods can be generalized to apply to other information visualization techniques.

A three-dimensional, multi-relational version of parallel coordinates is presented that enables a simultaneous analysis of all pairwise relationships between a single focus variable and all other variables included in the display. This approach permits a more rapid analysis of highly multivariate data sets. Through a number of user studies the multi-relational parallel coordinates technique has been evaluated against standard, two-dimensional parallel coordinates and been found to better support a number of different types of task.

High precision density maps and transfer functions are presented as a means to reveal structure in large data displayed in parallel coordinates. These two approaches make it possible to interactively analyse arbitrary regions in a parallel coordinates display without risking the loss of significant structure.

Another focus of this thesis relates to the visualization of time-varying, multivariate data. This has been studied both in the specific application area of system identification using volumetric representations, as well as in the general case by the introduction of temporal parallel coordinates.

The methods described in this thesis have all been implemented using modern computer graphics hardware which enables the display and manipulation of very large data sets in real time. A wide range of data sets, both synthetically generated and taken from real applications, have been used to test these methods. It is expected that, as long as the data have multivariate properties, they could be employed efficiently.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap , 2008.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1191
Keyword [en]
Information visualization, parallel coordinates, interactive analysis, multivariate data, time-varying data, GPU, hardware graphics
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-11643ISBN: 978-91-7393-878-5 (print)OAI: oai:DiVA.org:liu-11643DiVA: diva2:18059
Public defence
2008-05-30, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 09:15 (English)
Opponent
Supervisors
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2010-12-06
List of papers
1. 3-Dimensional Display for Clustered Multi-Relational Parallel Coordinates
Open this publication in new window or tab >>3-Dimensional Display for Clustered Multi-Relational Parallel Coordinates
2005 (English)In: Proceedings of IEEE International Conference on Information Visualisation, IV05, 6-8 July, 2005, 188-193 p.Conference paper, Published paper (Other academic)
Abstract [en]

Analysing multivariate data is a difficult task. Extensive interaction with the data is often necessary and, hence, the analysis can be quite time consuming. In this paper, we introduce a method to allow the user to simultaneously examine the relationships of a single dimension with many others in the data. The single dimension can then be interactively changed to allow the user to quickly examine all possible combinations. This method is achieved by extending the standard parallel coordinate approach to a 3D clustered multi-relational parallel coordinate representation (CMRPC). To aid this method, we use a technique called relation spacing which is used to position the axes according to how 'interesting' the different relations are. We also propose a number of interaction techniques to further facilitate the analysis process.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13210 (URN)10.1109/IV.2005.1 (DOI)
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2010-12-06
2. Task-Based Evaluation of Multi-Relational 3D and Standard 2D Parallel Coordinates
Open this publication in new window or tab >>Task-Based Evaluation of Multi-Relational 3D and Standard 2D Parallel Coordinates
2007 (English)In: Visualization and Data Analysis 2007 / [ed] Robert F. Erbacher, Jonathan C. Roberts, Matti T. Gröhn, Katy Börner, Bellingham, WA / Springfield, Virginia, USA: SPIE—The International Society for Optical Engineering & IS&T—The Society for Imaging Science and Technology , 2007, Vol. 6495, 64950C-1-64950C-12 p.Conference paper, Published paper (Refereed)
Abstract [en]

Multivariatedata sets exist in a wide variety of fields andparallel coordinates visualizations are commonly used for analysing such data.This paper presents a usability evaluation where we compare threetypes of parallel coordinates visualization for exploratory analysis of multivariatedata. We use a standard parallel coordinates display with manualpermutation of axes, a standard parallel coordinates display with automaticpermutation of axes, and a multi-relational 3D parallel coordinates displaywith manual permutation of axes. We investigate whether a 3Dlayout showing more relations simultaneously, but distorted by perspective effects,is advantageous when compared with a standard 2D layout. Theevaluation is accomplished by means of an experiment comparing performancedifferences for a class of task known to be well-supportedby parallel coordinates. Two levels of difficulty of the taskare used and both require the user to find relationshipsbetween variables in a multivariate data set. Our results showthat for the manual exploration of a complex interrelated multivariatedata set, the user performance with multi-relational 3D parallel coordinatesis significantly faster. In simpler tasks, however, the difference isnegligible. The study adds to the body of work examiningthe utility of 3D representations and what properties of structurein 3D space can be successfully used in 3D representationsof multivariate data.

Place, publisher, year, edition, pages
Bellingham, WA / Springfield, Virginia, USA: SPIE—The International Society for Optical Engineering & IS&T—The Society for Imaging Science and Technology, 2007
Series
Proceedings of SPIE - International Society for Optical Engineering, ISSN 0277-786X ; Vol. 6495
Keyword
Evaluation, parallel coordinates, multi-relational 3D parallel coordinates, multivariate data
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13211 (URN)10.1117/12.697548 (DOI)000245866700010 ()9780819466082 (ISBN)
Conference
Visualization and Data Analysis, San Jose, California, USA, 29–30 January 2007
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2014-04-23Bibliographically approved
3. Perceiving Patterns in Parallel Coordinates: Determining Thresholds for Identification of Relationships
Open this publication in new window or tab >>Perceiving Patterns in Parallel Coordinates: Determining Thresholds for Identification of Relationships
2008 (English)In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 7, no 2, 152-162 p.Article in journal (Refereed) Published
Abstract [en]

This article presents a study that investigates the ability of humans to perceive relationships (patterns) in parallel coordinates, an ability that is crucial to the use of this popular visualization technique. It introduces a visual quality metric, acceptable distortions of patterns, which establishes the level of noise that may be present in data while allowing accurate identification of patterns. This metric was used to assess perceptual performance of standard 2D parallel coordinates and multi-relational 3D parallel coordinates in two experiments. In multi-relational 3D parallel coordinates the axes are placed on a circle with a focus axis in the centre, allowing a simultaneous analysis between the focus variable and all other variables. The experiments aimed to determine the maximum number of variables that can be, from a user's point of view, efficiently used in a multi-relational 3D parallel coordinates display and to present a first attempt to study users' ability to analyse noisy data in parallel coordinates. The results show that, in terms of the acceptable level of noise in data, a multi-relational 3D parallel coordinates visualization having 11 axes (variables) is as efficient as standard 2D parallel coordinates. Visualizing a larger number of variables would possibly require a greater amount of manipulation of the visualization and thus be less efficient.

Keyword
Evaluation of visualization, parallel coordinates, pattern identification, perception
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13212 (URN)10.1057/palgrave.ivs.9500166 (DOI)
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2010-12-06
4. Revealing Structure within Clustered Parallel Coordinates Displays
Open this publication in new window or tab >>Revealing Structure within Clustered Parallel Coordinates Displays
2005 (English)In: Proceedings of IEEE Symposium on Information Visualization, 23-25 Oct., 2005, 125-132 p.Conference paper, Published paper (Other academic)
Abstract [en]

In order to gain insight into multivariate data, complex structures must be analysed and understood. Parallel coordinates is an excellent tool for visualizing this type of data but has its limitations. This paper deals with one of its main limitations - how to visualize a large number of data items without hiding the inherent structure they constitute. We solve this problem by constructing clusters and using high precision textures to represent them. We also use transfer functions that operate on the high precision textures in order to highlight different aspects of the cluster characteristics. Providing predefined transfer functions as well as the support to draw customized transfer functions makes it possible to extract different aspects of the data. We also show how feature animation can be used as guidance when simultaneously analysing several clusters. This technique makes it possible to visually represent statistical information about clusters and thus guides the user, making the analysis process more efficient.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13213 (URN)10.1109/INFVIS.2005.1532138 (DOI)
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2015-05-27
5. Revealing Structure in Visualizations of Dense 2D and 3D Parallel Coordinates
Open this publication in new window or tab >>Revealing Structure in Visualizations of Dense 2D and 3D Parallel Coordinates
2006 (English)In: Information Visualization, ISSN 1473-8716, Vol. 5, no 2, 125-136 p.Article in journal (Refereed) Published
Abstract [en]

Parallel coordinates is a well-known technique used for visualization of multivariate data. When the size of the data sets increases the parallel coordinates display results in an image far too cluttered to perceive any structure. We tackle this problem by constructing high-precision textures to represent the data. By using transfer functions that operate on the high-precision textures, it is possible to highlight different aspects of the entire data set or clusters of the data. Our methods are implemented in both standard 2D parallel coordinates and 3D multi-relational parallel coordinates. Furthermore, when visualizing a larger number of clusters, a technique called 'feature animation' may be used as guidance by presenting various cluster statistics. A case study is also performed to illustrate the analysis process when analysing large multivariate data sets using our proposed techniques.

Keyword
Parallel coordinates, 3D multi-relational parallel coordinates, clustering, transfer function, density map, feature animation
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-13214 (URN)10.1057/palgrave.ivs.9500117 (DOI)
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2015-05-27
6. Interactive Analysis of Time-Varying Systems Using Volume Graphics
Open this publication in new window or tab >>Interactive Analysis of Time-Varying Systems Using Volume Graphics
2004 (English)In: Proceedings of the 43rd IEEE Conference on Decision and Control, 2004, 5083-5087 p.Conference paper, Published paper (Refereed)
Abstract [en]

We show how 3-dimensional volume graphics can be used as a tool in system identification. Time-dependent dynamics often leave a significant residual with linear, time-invariant models. The structure of this residual is decisive for the subsequent modelling, and by using advanced visualization techniques, the modeller may gain a deeper insight into this structure than that which can be obtained from standard correlation analysis. We present a development platform that merges a rich variety of estimation programs with state of the art visualization techniques.

Keyword
System identification, Time-varying systems, Visualization techniques
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-13215 (URN)10.1109/CDC.2004.1429613 (DOI)0-7803-8682-5 (ISBN)
Conference
43rd IEEE Conference on Decision and Control, Paradise Island, Bahamas, December, 2004
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2013-03-26Bibliographically approved
7. Depth Cues and Density in Temporal Parallel Coordinates
Open this publication in new window or tab >>Depth Cues and Density in Temporal Parallel Coordinates
2007 (English)In: Proceedings of Eurographics/IEEE VGTC Symposium on Visualization, Norrköping, Sweden, Aire-la-Ville, Switzerland: Eurographics Association , 2007, 35-42 p.Conference paper, Published paper (Other academic)
Abstract [en]

This paper introduces Temporal Density Parallel Coordinates (TDPC) and Depth Cue Parallel Coordinates (DCPC) which extend the standard 2D parallel coordinates technique to capture time-varying dynamics. The proposed techniques can be used to analyse temporal positions of data items as well as temporal positions of changes occurring using 2D displays. To represent temporal changes, polygons (instead of traditional lines) are rendered in parallel coordinates. The results presented show that rendering polygons is superior at revealing large temporal changes. Both TDPC and DCPC have been efficiently implemented on the GPU allowing the visualization of thousands of data items over thousands of time steps at interactive frame rates.

Place, publisher, year, edition, pages
Aire-la-Ville, Switzerland: Eurographics Association, 2007
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-13216 (URN)10.2312/VisSym/EuroVis07/035-042 (DOI)
Available from: 2008-05-07 Created: 2008-05-07 Last updated: 2015-05-27

Open Access in DiVA

cover(69 kB)51 downloads
File information
File name COVER01.pdfFile size 69 kBChecksum SHA-1
ced546155ff095babd4141c88eaa728bce1ad901c9c7a8c7a7b1945f8c8ee41321ff9df7
Type coverMimetype application/pdf
fulltext(1493 kB)2087 downloads
File information
File name FULLTEXT01.pdfFile size 1493 kBChecksum SHA-1
a45b091bb1dc1873b3e3df007020431f7e6de47cc7756a875256ebf10f32561675257a10
Type fulltextMimetype application/pdf

Authority records BETA

Johansson, Jimmy

Search in DiVA

By author/editor
Johansson, Jimmy
By organisation
Visual Information Technology and Applications (VITA)The Institute of Technology
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
Total: 2087 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 3261 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf