Spatial Data Generators
| Autor: | Vu, Tin, Migliorini, Sara, Eldawy, Ahmed, Belussi, Alberto |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | 1st ACM SIGSPATIAL International Workshop on Spatial Gems (SpatialGems 2019) |
| Druh dokumentu: | Working Paper |
| Popis: | This gem describes a standard method for generating synthetic spatial data that can be used in benchmarking and scalability tests. The goal is to improve the reproducibility and increase the trust in experiments on synthetic data by using standard widely acceptable dataset distributions. In addition, this article describes how to assign a unique identifier to each synthetic dataset that can be shared in papers for reproducibility of results. Finally, this gem provides a supplementary material that gives a reference implementation for all the provided distributions. Comment: 9 pages |
| Databáze: | arXiv |
| Externí odkaz: |
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