Computer Technology Institute, P.O. Box 1122, 26110 Patras, Hellas, Brazil Institute of Computing, State University of Campinas P.O. Box 6176, 13083-970 Campinas SP, Brazil
Copyright 1999 INIST-CNRS CC BY 4.0 Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Sciences of information and communication. Documentation
FRANCIS
Accession Number:
edscal.1824271
Database:
PASCAL Archive
Weitere Informationen
An efficient benchmarking environment for spatiotemporal access methods should at least include modules for generating synthetic datasets, storing datasets (real datasets included), collecting and running access structures, and visualizing experimental results. Focusing on the dataset repository module, a collection of synthetic data that would simulate a variety of real life scenarios is required. Several algorithms have been implemented in the past to generate static spatial (point or rectangular) data, for instance, following a predefined distribution in the workspace. However, by introducing motion, and thus temporal evolution in spatial object definition, generating synthetic data tends to be a complex problem. In this paper, we discuss the parameters to be considered by a generator for such type of data, propose an algorithm, called Generate―Spatio―Temporal―Data<right single quotation mark> (GSTD), which generates sets of moving point or rectangular data that follow an extended set of distributions. Some actual generated datasets are also presented. The GSTD source code and several illustrative examples are currently available to all researchers through the Internet.
