BigGIS: A Continuous Refinement Approach to Master Heterogeneity and Uncertainty in Spatio-temporal Big Data (Vision Paper)

Resource type
Wiener, Patrick and Stein, Manuel and Seebacher, Daniel and Bruns, Julian and Frank, Matthias and Simko, Viliam and Zander, Stefan and Nimis, Jens
Book title
Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Geographic information systems (GIS) are important for decision support based on spatial data. Due to technical and economical progress an ever increasing number of data sources are available leading to a rapidly growing fast and unreliable amount of data that can be bene ficial (1) in the approximation of multivariate and causal predictions of future values as well as (2) in robust and proactive decision-making processes. However, today's GIS are not designed for such big data demands and require new methodologies to effectively model uncertainty and generate meaningful knowledge. As a consequence, we introduce BigGIS, a predictive and prescriptive spatio-temporal analytics platform, that symbiotically combines big data analytics, semantic web technologies and visual analytics methodologies. We present a novel continuous refinement model and show future challenges as an intermediate result of a collaborative research project into big data methodologies for spatio-temporal analysis and design for a big data enabled GIS.
Online Sources
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Published by
Patrick Wiener