The Retrieval of Regions with Similar Tendency in Geo-Tagged Dataset
We consider an application scenario where user want to find regions that have similar tendency about a certain issue, e.g., looking for regions that are neutral to new welfare policies. Motivated by this, we present a novel query to retrieve regions with similar tendency, named ρ-Dense Region Query (ρ-DR Query), that returns arbitrary shape of regions whose tendency satisfy the ρ-dense constraint. We design a basic algorithm to find all regions with similar spatial textual density that we define in this paper, and also propose an advanced algorithm that performs more efficiently. We conduct experiments to evaluate the performance of both algorithms, and the experiments prove the advanced algorithm is superior to the basic algorithm.
KeywordsGeo-tagged data Spatial textual query Region retrieval
This research was supported by the Korean MSIT(Ministry of Science and ICT), under the National Program for Excellence in SW(2015-0-00936) supervised by the IITP(Institute for Information & communications Technology Promotion).
- 1.Liu, J., Yu, G., Sun, H.: Subject-oriented top-k hot region queries in spatial dataset. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2409–2412. ACM (2011)Google Scholar
- 7.Zhang, D., Chee, Y.M., Mondal, A., Tung, A.K., Kitsuregawa, M.: Keyword search in spatial databases: towards searching by document. In: IEEE 25th International Conference on Data Engineering, 2009, ICDE 2009, pp. 688–699. IEEE (2009)Google Scholar
- 8.Zhang, D., Ooi, B.C., Tung, A.K.: Locating mapped resources in web 2.0. In: 2010 IEEE 26th International Conference on Data Engineering (ICDE), pp. 521–532. IEEE (2010)Google Scholar
- 9.Wu, D., Jensen, C.S.: A density-based approach to the retrieval of top-k spatial textual clusters. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2095–2100. ACM (2016) Google Scholar
- 10.Lu, J., Lu, Y., Cong, G.: Reverse spatial and textual k nearest neighbor search. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 349–360. ACM (2011)Google Scholar
- 12.Long, C., Wong, R.C.W., Wang, K., Fu, A.W.C.: Collective spatial keyword queries: a distance owner-driven approach. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 689–700. ACM (2013)Google Scholar
- 14.Wu, D., Yiu, M.L., Jensen, C.S., Cong, G.: Efficient continuously moving top-k spatial keyword query processing. In: 2011 IEEE 27th International Conference on Data Engineering (ICDE), pp. 541–552. IEEE (2011)Google Scholar
- 16.Ni, J., Ravishankar, C.V.: Pointwise-dense region queries in spatio-temporal databases. In: IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007, pp. 1066–1075. IEEE (2007)Google Scholar
- 17.Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 373–384. ACM (2011)Google Scholar
- 18.Souvaine, D.: Line Segment Intersection using a Sweep Line Algorithm. Tufts University, Medford (2005)Google Scholar
- 19.Rigaux, P., Scholl, M., Voisard, A.: Spatial Databases: With Application to GIS. Elsevier, Amsterdam (2001)Google Scholar