GEOSPATIAL EXPAT PARSER USING K-MEANS FOR QUERYING OPEN STREET MAP

Alka Setiya , Rachna Behl

Abstract


The importance and motivations behind geospatial data extraction has been changing and developing step by step, energized more commitment by the OSM. This development gave progressive techniques for sharing furthermore, processing information by crowd-sourcing, for example, OSM, likewise called "the wikification of maps" by a few analysts. At the point when crowd-sourcing gathers immense information which withhold in corpus data,  with help of overall territorial jurisdiction with fluctuating level of mapping background, the concentration of this scheme ought to be on breaking down the information as opposed to gathering it, using semantic parser we provide the scheme to query OSM information by looking at it with restrictive information or information of administrative guide offices and surveys the exploration work for appraisal of OSM  and furthermore talks about the future bearings  using Machine Readable Language. Therefore, in this scheme we propose to use the corpus data more than 500 MB from maps to extract an accurate semantic structure that will build the basis of a natural language interface to OSM. Furthermore, we use response-based learning on parser results to adapt a statistical machine translation system for relational database access to OSM.

Keywords


OSM (Open Street Maps), geospatial data, machine readable language, fuzzy natural language. statistical machine translation, semantic parsing.

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References


Graham, M.; Hale, S.; Stephens, M. Digital Divide: “The Geography of Internet Access. Environ. Plan”. A 2012, 44, 1009–1010.

Jokar Arsanjani, J.; Helbich, M.; Bakillah, M.; Loos, L. “The Emergence and Evolution of OpenStreetMap: A Cellular Automata Approach”. Int. J. Digit. Earth 2013 b, 00, 1–15

Weiwei Sun, Chong Chen, “Merged aggregate nearest neighbor query processing in road network”, Singapore Management University Institutional Knowledge at Singapore Management University, 10-2013.

Zitong Chen, Yubao Liu, Raymond Chi-Wing Wong, “Efficient Algorithms for Optimal Location Queries in Road Networks”, The Hong Kong University of Science and Technology, Hong Kong, China.

Aye Su Yee Win, “Fast Algorithm for Multi-type Nearest Neighbor Quer”, Graduate School of Science and Engineering, Saitama University, D-002.

Lingkun Wu, Xiaokui Xiao, “Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation”, School of Computer Engineering , Nanyang Technological University, Singapore.

L. Alarabi, A. Eldawy, R. Alghamdi, and M. F. Mokbel. TAREEG: A MapReduce-Based Web Service for Extracting Spatial Data from OpenStreetMap (System Demonstration). In SIGMOD, pages 897–900, Snowbird, UT, June 2014.

Z. Chen, Y. Liy, R. C.-W. Wong, J. Xiong, G. Mai, and C. Long. “Efficient algorithms for optimal location queries in road networks”. In SIGMOD, 2014.

Z. Chen, H. T. Shen, X. Zhou, and J. X. Yu. Monitoring path nearest neighbor in road networks. In SIGMOD, pages 591–602, 2009.

K. Deng, X. Zhou, and H. T. Shen. “Multi-source skyline query processing in road networks”. In ICDE, pages 796–805, 2007.

A. Eldawy and M. F. Mokbel. A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data (System Demo). In VLDB, Riva del Garda, Italy, Aug. 2013.

Z. Chen, Y. Liy, R. C.-W. Wong, J. Xiong, G. Mai, and C. Long. “Efficient algorithms for optimal location queries in road networks”s. In SIGMOD, 2014.

Z. Chen, H. T. Shen, X. Zhou, and J. X. Yu. Monitoring path nearest neighbor in road networks. In SIGMOD, pages 591–602, 2009.

K. Deng, X. Zhou, and H. T. Shen. “Multi-source skyline query processing in road networks”. In ICDE, pages 796–805, 2007.

A. Eldawy and M. F. Mokbel. A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data (System Demo). In VLDB, Riva del Garda, Italy, Aug. 2013.


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