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| Content Provider | Springer Nature Link |
|---|---|
| Author | Alkobaisi, Shayma Bae, Wan D. Vojtěchovský, Petr Narayanappa, Sada |
| Copyright Year | 2011 |
| Abstract | Many Geographic Information Systems (GIS) handle a large volume of geospatial data. Spatial joins over two or more geospatial datasets are very common operations in GIS for data analysis and decision support. However, evaluating spatial joins can be very time intensive due to the size of datasets. In this paper, we propose an interactive framework that provides faster approximate answers of spatial joins. The proposed framework utilizes two statistical methods: probabilistic join and sampling based join. The probabilistic join method provides speedup of two orders of magnitude with no correctness guarantee, while the sampling based method provides an order of magnitude improvement over the full indexing tree joins of datasets and also provides running confidence intervals. The framework allows users to trade-off speed versus bounded accuracy, hence it provides truly interactive data exploration. The two methods are evaluated empirically with real and synthetic datasets. |
| Starting Page | 329 |
| Ending Page | 355 |
| Page Count | 27 |
| File Format | |
| ISSN | 13846175 |
| Journal | GeoInformatica |
| Volume Number | 16 |
| Issue Number | 2 |
| e-ISSN | 15737624 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2011-08-19 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Interactive queries Spatial join Join probability Probabilistic joins Incremental sampling Quad-tree R-tree GIS Information Storage and Retrieval Data Structures, Cryptology and Information Theory Multimedia Information Systems Computer Science Geographical Information Systems/Cartography |
| Content Type | Text |
| Resource Type | Article |
| Subject | Geography, Planning and Development Information Systems |
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