NDLI: The spatiotemporal multivariate hypercube for discovery of patterns in event data

Content Provider	IEEE Xplore Digital Library
Author	Olislagers, F. Worring, M.
Copyright Year	2012
Description	Author affiliation: Intelligent Systems Lab Amsterdam, University of Amsterdam, The Netherlands (Olislagers, F.; Worring, M.)
Abstract	Event data can hold valuable decision making information, yet detecting interesting patterns in this type of data is not an easy task because the data is usually rich and contains spatial, temporal as well as multivariate dimensions. Research into visual analytics tools to support the discovery of patterns in event data often focuses on the spatiotemporal or spatiomultivariate dimension of the data only. Few research efforts focus on all three dimensions in one framework. An integral view on all three dimensions is, however, required to unlock the full potential of event datasets. In this poster, we present an event visualization, transition, and interaction framework that enables an integral view on all dimensions of spatiotemporal multivariate event data. The framework is built around the notion that the event data space can be considered a spatiotemporal multivariate hypercube. Results of a case study we performed suggest that a visual analytics tool based on the proposed framework is indeed capable to support users in the discovery of multidimensional spatiotemporal multivariate patterns in event data.
Starting Page	235
Ending Page	236
File Size	377400
Page Count	2
File Format	PDF
ISBN	9781467347525
e-ISBN	9781467347532
DOI	10.1109/VAST.2012.6400536
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multidimensional data Visual analytics Coordinated and multiple views Data visualization Abstracts Field studies Hypercubes Educational institutions Spatiotemporal phenomena Intelligent systems Visual knowledge discovery
Content Type	Text
Resource Type	Article

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