Experiments to Distribute and Parallelize Map Generalization Processes

Guillaume Touya (1) , Justin Berli , Imran Lokhat (1) , Nicolas Regnauld

1 LaSTIG - Laboratoire des Sciences et Technologies de l'Information Géographique

Automatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but less effective when taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context.

Mots clés: parallelization; partitioning; generalization

Mots clés: Généralisation

Domaines: Informatique [cs]

Guillaume Touya, Justin Berli, Imran Lokhat, Nicolas Regnauld. Experiments to Distribute and Parallelize Map Generalization Processes. The Cartographic Journal, 2018, 54 (4), pp.322-332. ⟨10.1080/00087041.2017.1413787⟩. ⟨hal-02155541⟩