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Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data

Bernard, Jürgen and Sessler, David and Steiger, Martin and Spott, Martin and Kohlhammer, Jörn (2016):
Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data.
In: EuroVA 2016, 7th international EuroVis workshop on Visual Analytics, Groningen, The Netherlands, June 6-7, 2016., DOI: 10.2312/eurova.20161124, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Bernard, Jürgen and Sessler, David and Steiger, Martin and Spott, Martin and Kohlhammer, Jörn
Title: Visual-Interactive Exploration of Relations Between Time-Oriented Data and Multivariate Data
Language: English
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.

Uncontrolled Keywords: 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
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: EuroVA 2016, 7th international EuroVis workshop on Visual Analytics
Event Location: Groningen, The Netherlands
Event Dates: June 6-7, 2016.
Date Deposited: 08 May 2019 06:39
DOI: 10.2312/eurova.20161124
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