Managing Uncertainty in Evolving Geo-Spatial Data

被引:4
|
作者
Zufle, Andreas [1 ]
Trajcevski, Goce [2 ]
Pfoser, Dieter [1 ]
Kim, Joon-Seok [1 ]
机构
[1] George Mason Univ, Dept Geog & Geoinformat Sci, Fairfax, VA 22030 USA
[2] Iowa State Univ, Dept Elect & Comp Engn, Ames, IA USA
基金
美国国家科学基金会;
关键词
OPENSTREETMAP; QUERIES;
D O I
10.1109/MDM48529.2020.00021
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:5 / 8
页数:4
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