The spatio-temporal database research community has just started to investigate benchmarking issues. On one hand we would rather have a benchmark that is representative of real world applications, in order to verify the expressiveness of proposed models. On the other hand, we would like a benchmark that offers a sizeable workload of data and query sets, which could obviously stress the strengths and weaknesses of a broad range of data access methods. This paper offers a framework for spatio-temporal data sets generator, a first step towards a full benchmark for the large real world application field of "smoothly" moving objects with few or no restrictions in motion. The driving application is the modelling of fishing ships where the ships go in the direction of the most attractive shoals of fish while trying to avoid storm areas. Fishes are themselves attracted by plankton areas. Ships are moving points; plankton or storm areas are regions with fixed centre but moving shape; and shoals are moving regions. The specification is written in such a way that the users can easily adjust generation model parameters.

The fields of application of spatio-temporal systems, i.e., systems that must operate with time-varying spatial properties, are vast and heterogeneous. Since it would be difficult to treat such diversity as a whole, we introduce a classification for spatio-temporal systems based on the properties of the represented objects. Building on this classification, we also claim that features of some complex objects can be derived from those of simpler ones, suggesting an evolutionary approach, starting with the study of simple objects and progressing by enriching them with new features. This paper focuses on the definition of a data model for representation of moving points. The model is based on the decomposition of the trajectory of moving points into sections. The movement within each section of a trajectory is described by a variability function. Since, for most systems, it is not possible to store the exact knowledge about the movement of a mobile, the answers to queries may be imprecise. We propose two additional approaches to deal with imprecision, the superset and the subset semantics, based on a maximum value for the variability function, and a smooth technique to integrate them in the model. Finally, we analyse some functional aspects of the implementation of the data model on a Relational Database Management System (RDBMS) and outline some directions for future research.

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