An extendable framework for managing uncertain spatio-temporal data

This demonstration presents our Uncertain-Spatio-Temporal (UST)} framework that we have developed in recent years. The framework allows not only to visualize and explore spatio-temporal data consisting of (location, time, object)-triples but also provides an extensive codebase easily extensible and customizable by developers and researchers. The main research focus of this UST-framework is the explicit consideration of uncertainty, an aspect that is inherent in spatio-temporal data, due to infrequent position updates, due to physical limitations and due to power constraints. The UST-framework can be used to obtain a deeper intuition of the quality of spatio-temporal data models. Such models aim at estimating the position of a spatio-temporal object at a time where the object's position is not explicitly known, for example by using both historic (traffic-) pattern information, and by using explicit observations of objects. The UST-framework illustrates the resulting distributions by allowing a user to move forward and backward in time. Additionally the framework allows users to specify simple spatio-temporal queries, such as spatio-temporal window queries and spatio-temporal nearest neighbor (NN) queries. Based on recently published theoretic concepts, the UST-framework allows to visually explore the impact of different models and parameters on spatio-temporal data. The main result showcased by the UST-framework is a minimization of uncertainty by employing stochastic processes, leading to small expected distances between ground truth trajectories and modelled positions.

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