Managing Uncertainty in Evolving Geo-Spatial Data

Our ability to extract knowledge from evolving spatial phenomena and make it actionable is often impaired by unreliable, erroneous, obsolete, imprecise, sparse, and noisy data. Integrating the impact of this uncertainty is a paramount when estimating the reliability/confidence of any time-varying qu...

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Bibliographic Details
Published in2020 21st IEEE International Conference on Mobile Data Management (MDM) pp. 5 - 8
Main Authors Zufle, Andreas, Trajcevski, Goce, Pfoser, Dieter, Kim, Joon-Seok
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2020
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Summary:Our ability to extract knowledge from evolving spatial phenomena and make it actionable is often impaired by unreliable, erroneous, obsolete, imprecise, sparse, and noisy data. Integrating the impact of this uncertainty is a paramount when estimating the reliability/confidence of any time-varying query result from the underlying input data. The goal of this advanced seminar is to survey solutions for managing, querying and mining uncertain spatial and spatio-temporal data. We survey different models and show examples of how to efficiently enrich query results with reliability information. We discuss both analytical solutions as well as approximate solutions based on geosimulation.
ISSN:2375-0324
DOI:10.1109/MDM48529.2020.00021