ACM Transactions on Database Systems (TODS)
OOPLSA '86 Conference proceedings on Object-oriented programming systems, languages and applications
Multi-table joins through bitmapped join indices
ACM SIGMOD Record
GeoMiner: a system prototype for spatial data mining
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Spot: distance based join indices for spatial data
Proceedings of the 7th ACM international symposium on Advances in geographic information systems
A survey of spatial data mining methods databases and statistics point of views
Proceedings of the 2000 information resources management association international conference on Challenges of information technology management in the 21st century
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the Seventh International Conference on Data Engineering
Distance-Associated Join Indices for Spatial Range Search
Proceedings of the Eighth International Conference on Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Topological Relations Between Regions in R² and Z²
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
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ACM SIGKDD Explorations Newsletter
STING+: An Approach to Active Spatial Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Theory of Relational Databases
Theory of Relational Databases
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The growing production of maps is generating huge volume of data stored in large spatial databases. This huge volume of data exceeds the human analysis capabilities. Spatial data mining methods, derived from data mining methods, allow the extraction of knowledge from these large spatial databases, taking into account the essential notion of spatial dependency. This paper focuses on this specificity of spatial data mining by showing the suitability of join indices to this context. It describes the join index structure and shows how it could be used as a tool for spatial data mining. Thus, this solution brings spatial criteria support to non-spatial information systems.