The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Keyword Search on Spatial Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Keyword Search in Spatial Databases: Towards Searching by Document
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Spatial keyword query processing: an experimental evaluation
Proceedings of the VLDB Endowment
Map search via a factor graph model
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The efficient execution of multi-criteria queries has gained increasing interest over the last years. In the present paper we propose an R-tree based approach for queries addressing textual as well as geographic filter conditions. Whereas most previous approaches use an index structure optimised for a single criterion adding special treatment for the other criterion at the leaf nodes or end points of this index structure, our approach uses a deeper integration. In short, R-trees are maintained for certain subsets of the whole term set. Furthermore, in each of these R-trees bit sets are used within the nodes to indicate whether entries for the terms associated with the single bits can be found in the corresponding sub-tree. Our index structure aims to be both, time and space efficient. The paper investigates the efficiency and applicability of the proposed index structure via practical experiments based on real-world and synthetic data.