Bernard, Jürgen ; Sessler, David ; Steiger, Martin ; Spott, Martin ; Kohlhammer, Jörn (2016)
Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data.
EuroVA 2016, 7th international EuroVis workshop on Visual Analytics. Groningen, The Netherlands (06.06.2016-07.06.2016)
doi: 10.2312/eurova.20161124
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The analysis of large, multivariate data sets is challenging, especially when some of these data objects are timeoriented. Exploring relationships between multivariate and temporal information, e.g., to identify patterns that support decision making is an important industrial analysis task. The target group of this design study are data analysts aiming at detecting fault patterns in a telecommunications network in order to spend maintenance budget more effectively. We present a visual analytics tool that provides overviews of multivariate data sets and associated time series. Users can select data subsets of interest in both attribute data and clustered time series data. Linked views consequently support the identification of relations between the two spaces. To ensure usefulness, the tool was designed in an iterative way, based on a careful characterization of the data, users, and tasks. A usage scenario demonstrates the applicability of the approach.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Bernard, Jürgen ; Sessler, David ; Steiger, Martin ; Spott, Martin ; Kohlhammer, Jörn |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2016 |
| Veranstaltungstitel: | EuroVA 2016, 7th international EuroVis workshop on Visual Analytics |
| Veranstaltungsort: | Groningen, The Netherlands |
| Veranstaltungsdatum: | 06.06.2016-07.06.2016 |
| DOI: | 10.2312/eurova.20161124 |
| Kurzbeschreibung (Abstract): | The analysis of large, multivariate data sets is challenging, especially when some of these data objects are timeoriented. Exploring relationships between multivariate and temporal information, e.g., to identify patterns that support decision making is an important industrial analysis task. The target group of this design study are data analysts aiming at detecting fault patterns in a telecommunications network in order to spend maintenance budget more effectively. We present a visual analytics tool that provides overviews of multivariate data sets and associated time series. Users can select data subsets of interest in both attribute data and clustered time series data. Linked views consequently support the identification of relations between the two spaces. To ensure usefulness, the tool was designed in an iterative way, based on a careful characterization of the data, users, and tasks. A usage scenario demonstrates the applicability of the approach. |
| Freie Schlagworte: | Guiding Theme: Digitized Work, Guiding Theme: Smart City, Research Area: Human computer interaction (HCI), Forschungsgruppe Semantic Models, Immersive Systems (SMIS), User interfaces, User-centered design, Visual analytics, Visual data mining, Information visualization, Multimodality |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 08 Mai 2019 06:39 |
| Letzte Änderung: | 08 Mai 2019 06:39 |
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