No abstract

The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

No abstract

Spatio-temporal data may be used to represent the evolution of real-world objects and phenomena. Such data can be represented in discrete time, which associates spatial information (like position and shape) to time instants, or in continuous time, in which the representation of the evolution of the phenomena is decomposed into slices and interpolation functions are used to estimate the intermediate position and shape at any time. The use of a discrete model may seem more straightforward, but a continuous representation provides potential gains in terms of data management, including in compression and spatio-temporal operations. In this work, we study the use of the continuous model to represent deformable moving regions captured at discrete snapshots. We use a dissimilarity distance-based strategy to select the observations that should be used to define the time slices of the continuous representation, thus transforming data acquired at discrete steps into a continuous model. We also study how the use of geometry simplification algorithms and simplification levels may impact on moving regions interpolation quality. We evaluate our proposals using a dataset composed by thousands of aerial bush-fires images. After applying object simplification and slice decomposition, we use two region interpolation algorithms to generate in-between observations and compare them with geometries representing real images. The results prove the effectiveness of our proposals and their importance in terms of interpolation accuracy.

Moving region is an abstraction used to represent the spatio-temporal behavior of real-world phenomena in database systems. The most common approach to model moving regions uses geometries to represent their position and shape at different times (observations), and interpolation functions to generate the evolution of the geometries between observations. Several region interpolation methods have been proposed in the databases literature, but as there is no suitable method for all use cases, users must select the most adequate algorithm to represent each region by visual inspection. This can be infeasible when dealing with large datasets. This paper presents the first steps towards a system that suggests which methods (and configurations) can generate representations fitting the requirements of a particular application. It includes an abstract specification of user-defined rules on the spatio-temporal evolution of moving regions to assess the suitability of region interpolation functions, a discussion on optimization strategies for efficient implementation of the rules and illustrative examples using real-world data to show how to use this approach to select the best methods to represent a spatio-temporal phenomena. CCS CONCEPTS • Information systems → Temporal data; Spatial-temporal systems.