Density-based spatial keyword querying |
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| Affiliation: | 1. Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, China;2. Nanjing University of Posts and Telecommunications, Nanjing, China;3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;1. GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, School of Information Science and Technology, Yunnan Normal University, Yunnan 650092, China;2. The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, School of Measurement-Control Tech & Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China;1. College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China;2. Department of Civil and Architectural Engineering, City University of Hong Kong, Kowloon, Hong Kong;1. Sapienza University of Rome, Department of Environmental Biology, P. le Aldo Moro, 5, 00185 Rome, Italy;2. Department of Science for Nature and Environmental Resources, University of Sassari, Sassari, Italy;3. Euro-Mediterranean Center on Climate Change (CMCC), Impacts on Agriculture, Forests and Natural Ecosystems (IAFES) Division, Sassari, Italy;1. Department of Architecture, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan;2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan;1. University of Tübingen, Plant Ecology Group, Institute of Evolution and Ecology, Auf der Morgenstelle 5, D-72076 Tübingen, Germany;2. University of Potsdam, Plant Ecology & Nature Conservation Group, Institute of Biochemistry and Biology, Maulbeerallee 3, 14469 Potsdam, Germany |
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| Abstract: | With the rocket development of the Internet, WWW(World Wide Web), mobile computing and GPS (Global Positioning System) services, location-based services like Web GIS (Geographical Information System) portals are becoming more and more popular. Spatial keyword queries over GIS spatial data receive much more attention from both academic and industry communities than ever before. In general, a spatial keyword query containing spatial location information and keywords is to locate a set of spatial objects that satisfy the location condition and keyword query semantics. Researchers have proposed many solutions to various spatial keyword queries such as top-K keyword query, reversed kNN keyword query, moving object keyword query, collective keyword query, etc. In this paper, we propose a density-based spatial keyword query which is to locate a set of spatial objects that not only satisfies the query’s textual and distance condition, but also has a high density in their area. We use the collective keyword query semantics to find in a dense area, a group of spatial objects whose keywords collectively match the query keywords. To efficiently process the density based spatial keyword query, we use an IR-tree index as the base data structure to index spatial objects and their text contents and define a cost function over the IR-tree indexing nodes to approximately compute the density information of areas. We design a heuristic algorithm that can efficiently prune the region according to both the distance and region density in processing a query over the IR-tree index. Experimental results on datasets show that our method achieves desired results with high performance. |
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| Keywords: | Density Spatial database Keyword query IR-tree index |
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