ML Anthology

Hierarchical Constraint Satisfaction in Spatial Databases

Dimitris Papadias, Panos Kalnis, Nikos Mamoulis
AAAI 1999 pp. 142-147
/aaai/1999/papadias1999aaai-hierarchical/

Abstract

Several content-based queries in spatial databases and geographic information systems (GISs) can be modelled and processed as constraint satisfaction problems (CSPs). Regular CSP algorithms, however, work for main memory retrieval without utilizing indices to prune the search space. This paper shows how systematic and local search techniques can take advantage of the hierarchical decom-position of space, preserved by spatial data structures, to efficiently guide search. We study the conditions under which hierarchical constraint satisfaction outperforms tra-ditional methods with extensive experimentation.

PDF AAAI Semantic Scholar

Cite

Text

Papadias et al. "Hierarchical Constraint Satisfaction in Spatial Databases." AAAI Conference on Artificial Intelligence, 1999.

Markdown

[Papadias et al. "Hierarchical Constraint Satisfaction in Spatial Databases." AAAI Conference on Artificial Intelligence, 1999.](https://mlanthology.org/aaai/1999/papadias1999aaai-hierarchical/)

BibTeX

@inproceedings{papadias1999aaai-hierarchical,
  title     = {{Hierarchical Constraint Satisfaction in Spatial Databases}},
  author    = {Papadias, Dimitris and Kalnis, Panos and Mamoulis, Nikos},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {1999},
  pages     = {142-147},
  url       = {https://mlanthology.org/aaai/1999/papadias1999aaai-hierarchical/}
}