R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Learning Recursive Theories in the Normal ILP Setting
Fundamenta Informaticae
The Life of a Logic Programming System
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Novelty Detection from Evolving Complex Data Streams with Time Windows
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Spatial-yap: a logic-based geographic information system
ICLP'07 Proceedings of the 23rd international conference on Logic programming
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Wildfires can importantly affect the ecology and economy of large regions of the world. Effective prevention techniques are fundamental to mitigate their consequences. The design of such preemptive methods requires a deep understanding of the factors that increase the risk of fire, particularly when we can intervene on these factors. This is the case for the maintenance of ecological balances in the landscape that minimize the occurrence of wildfires. We use an inductive logic programming approach over detailed spatial datasets: one describing the landscape mosaic and characterizing it in terms of its use; and another describing polygonal areas where wildfires took place over several years. Our inductive process operates over a logic term representation of vectorial geographic data and uses spatial predicates to explore the search space, leveraging the framework of Spatial-Yap, its multi-dimensional indexing and tabling extensions.We show that the coupling of a logic-based spatial database with an inductive logic programming engine provides an elegant and powerful approach to spatial data mining.