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
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Inverted files versus signature files for text indexing
ACM Transactions on Database Systems (TODS)
Direct spatial search on pictorial databases using packed R-trees
SIGMOD '85 Proceedings of the 1985 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
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
Hybrid index structures for location-based web search
Proceedings of the 14th ACM international conference on Information and knowledge management
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
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
Efficient retrieval of the top-k most relevant spatial web objects
Proceedings of the VLDB Endowment
Hadoop: The Definitive Guide
IR-Tree: An Efficient Index for Geographic Document Search
IEEE Transactions on Knowledge and Data Engineering
Hi-index | 0.00 |
Spatial keyword queries, finding objects closest to a specified location that contains a set of keywords, are a kind of pervasive operations in spatial databases. In reality, there is some spatial data that is not stored in databases, instead in files. And generally this kind of spatial data is textual, noisy, large-scale and used now and then, which makes it be quite costly to conduct spatial keyword querying on such spatial data. To solve this problem, in this paper we propose an efficient method by using a distributed system based on MapReduce. The algorithm for spatial keyword query evaluation under the MapReduce model is developed and implemented. Experimental results demonstrate that this method can process spatial keyword queries effectively and efficiently.