Generating semantics-based trajectories of moving objects
The domain of spatiotemporal applications is a treasure trove of new types of data as well as queries. For designing and testing novel data types and access methods that will support these applications, large spatiotemporal datasets are needed. As in many cases it is not possible to obtain real datasets, either they do not exist or they are not accessible, synthetic datasets are governed by artificial parameters rather than by the (sometimes invisible) rules governing real-world behavior. In this work, we show how, at least for some cases, one can generate spatiotemporal datasets that simulate real-world behavior. We illustrate example cases and translate them into appropriate calls of GSTD2 a spatiotemporal data generator. The generated data is illustrated using two-dimensional snapshot pictures as well as three-dimensional (two spatial plus one temporal dimension) trajectory images.
moving objects spatiotemporal datasets data generation synthetic data