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Geovisual Analytics for Self-Organizing Network Data
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Information Visualization)
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Information Visualization)
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. (Information Visualization)
2009 (English)In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology, 2009 (VAST 2009 / [ed] John Stasko, Jarke J. van Wijk, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331 USA: IEEE Service Center , 2009, 43-50 p.Conference paper, Published paper (Refereed)
Abstract [en]

Cellular radio networks are continually growing in both node count and complexity. It therefore becomes more difficult to manage the networks and necessary to use time and cost effective automatic algorithms to organize the network’s neighbor cell relations. There have been a number of attempts to develop such automatic algorithms. Network operators, however, may not trust them because they need to have an understanding of their behavior and of their reliability and performance, which is not easily perceived. This paper presents a novel web-enabled geovisual analytics approach to exploration and understanding of self-organizing network data related to cells and neighbor cell relations. A demonstrator and case study are presented in this paper, developed in close collaboration with the Swedish telecom company Ericsson and based on large multivariate, time-varying and geospatial data provided by the company. It allows the operators to follow, interact with and analyze the evolution of a self-organizing network and enhance their understanding of how an automatic algorithm configures locally-unique physical cell identities and organizes neighbor cell relations of the network. The geovisual analytics tool is tested with a self-organizing network that is operated by the Automatic Neighbor Relations (ANR) algorithm. The demonstrator has been tested with positive results by a group of domain experts from Ericsson and will be tested in production.

Place, publisher, year, edition, pages
445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331 USA: IEEE Service Center , 2009. 43-50 p.
Keyword [en]
Geovisual analytics, visualization, self-organizing network, multi-layer, multi-dimensional, time-varying, geospatial data sets.
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-52358DOI: 10.1109/VAST.2009.5332610ISBN: 978-1-4244-5283-5 (print)OAI: oai:DiVA.org:liu-52358DiVA: diva2:281713
Conference
IEEE Symposium on Visual Analytics Science and Technology, 2009 (VAST 2009), 11 - 16 October, Atlantic City, New Jersey, USA
Projects
VoSON
Available from: 2010-02-12 Created: 2009-12-17 Last updated: 2013-04-30Bibliographically approved
In thesis
1. Architecture and Applications of a Geovisual Analytics Framework
Open this publication in new window or tab >>Architecture and Applications of a Geovisual Analytics Framework
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The large and ever-increasing amounts of multi-dimensional, multivariate, multi-source, spatio-temporal data represent a major challenge for the future. The need to analyse and make decisions based on these data streams, often in time-critical situations, demands integrated, automatic and sophisticated interactive tools that aid the user to manage, process, visualize and interact with large data spaces. The rise of `Web 2.0', which is undisputedly linked with developments such as blogs, wikis and social networking, and the internet usage explosion in the last decade represent another challenge for adapting these tools to the Internet to reach a broader user community. In this context, the research presented in this thesis introduces an effective web-enabled geovisual analytics framework implemented, applied and verified in Adobe Flash ActionScript and HTML5/JavaScript. It has been developed based on the principles behind Visual Analytics and designed to significantly reduce the time and effort needed to develop customized web-enabled applications for geovisual analytics tasks and to bring the benefits of visual analytics to the public. The framework has been developed based on a component architecture and includes a wide range of visualization techniques enhanced with various interaction techniques and interactive features to support better data exploration and analysis. The importance of multiple coordinated and linked views is emphasized and a number of effective techniques for linking views are introduced.

Research has so far focused more on tools that explore and present data while tools that support capturing and sharing gained insight have not received the same attention. Therefore, this is one of the focuses of the research presented in this thesis. A snapshot technique is introduced, which supports capturing discoveries made during the exploratory data analysis process and can be used for sharing gained knowledge.

The thesis also presents a number of applications developed to verify the usability and the overall performance of the framework for the visualization, exploration and analysis of data in different domains. Four application scenarios are presented introducing (1) the synergies among information visualization methods, geovisualization methods and volume data visualization methods for the exploration and correlation of spatio-temporal ocean data, (2) effective techniques for the visualization, exploration and analysis of self-organizing network data, (3) effective flow visualization techniques applied to the analysis of time-varying spatial interaction data such as migration data, commuting data and trade flow data, and (4) effective techniques for the visualization, exploration and analysis of flood data.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2013. 78 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1511
Keyword
GeoVisual Analytics, toolkits, frameworks, web-enabled, visualization, interactive visualizations, interaction, interaction techniques, visual data analysis, component architecture, storytelling
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-91679 (URN)978-91-7519-643-5 (ISBN)
Public defence
2013-05-29, Domteatern, Visualiseringscenter C, Kungsgatan 54, Norrköping, 10:01 (English)
Opponent
Supervisors
Available from: 2013-04-29 Created: 2013-04-29 Last updated: 2013-04-30Bibliographically approved

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Ho, QuanÅström, TobiasJern, Mikael

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