Abstract
We consider the problem of finding map regions that best match query keywords. This region search problem can be applied in many practical scenarios such as shopping recommendation, searching for tourist attractions, and collision region detection for wireless sensor networks. While conventional map search retrieves isolate locations in a map, users frequently attempt to find regions of interest instead, e.g., detecting regions having too many wireless sensors to avoid collision, or finding shopping areas featuring various merchandise or tourist attractions of different styles. Finding regions of interest in a map is a non-trivial problem and retrieving regions of arbitrary shapes poses particular challenges. In this paper, we present a novel region search algorithm, dense region search (DRS), and its extensions, to find regions of interest by estimating the density of locations containing the query keywords in the region. Experiments on both synthetic and real-world datasets demonstrate the effectiveness of our algorithm.
Similar content being viewed by others
References
Aggarwal, A., Imai, H., Katoh, N., et al., 1989. Finding k points with minimum spanning trees and related problems. Proc. 5th Annual Symp. on Computational Geometry, p.283–291. https://doi.org/10.1145/73833.73865
Agrawal, R., Gehrke, J., Gunopulos, D., et al., 1998. Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD Rec., 27(2): 94–105. https://doi.org/10.1145/276304.276314
Ankerst, M., Breunig, M.M., Kriegel, H.P., et al., 1999. Optics: ordering points to identify the clustering structure. SIGMOD Rec., 28(2): 49–60. https://doi.org/10.1145/304182.304187
Aurenhammer, F., 1991. Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput. Surv., 23(3): 345–405. https://doi.org/10.1145/116873.116880
Chen, L.S., Cong, G., Jensen, C.S., et al., 2013. Spatial keyword query processing: an experimental evaluation. Proc. VLDB Endowm., 6(3): 217–228. https://doi.org/10.14778/2535569.2448955
Chen, Y.Y., Suel, T., Markowetz, A., 2006. Efficient query processing in geographic web search engines. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.277–288. https://doi.org/10.1145/1142473.1142505
Cheng, C.H., Fu, A.W., Zhang, Y., 1999. Entropy-based subspace clustering for mining numerical data. Proc. 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.84–93. https://doi.org/10.1145/312129.312199
Christoforaki, M., He, J., Dimopoulos, C., et al., 2011. Text vs. space:efficient geo-search query processing. Proc. 20th ACM Int. Conf. on Information and Knowledge Management, p.423–432. https://doi.org/10.1145/2063576.2063641
Cong, G., Jensen, C.S., Wu, D.M., 2009. Efficient retrieval of the top-k most relevant spatial web objects. Proc. VLDB Endowm., 2(1): 337–348. https://doi.org/10.14778/1687627.1687666
Ester, M., Kriegel, H.P., Sander, J., et al., 1996. A densitybased algorithm for discovering clusters in large spatial databases with noise. Proc. 2nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.226–231.
Fan, J., Li, G.L., Zhou, L.Z., et al., 2012. SEAL: spatiotextual similarity search. Proc. VLDB Endowm., 5(9): 824–835. https://doi.org/10.14778/2311906.2311910
Feige, U., Seltser, M., 1997. On the densest k-subgraph problem. Technical Report, the Weizmann Institute, Rehovot.
Feige, U., Kortsarz, G., Peleg, D., 2001. The dense ksubgraph problem. Algorithmica, 29: 410–421. https://doi.org/10.1007/s004530010050
Guo, D.S., Peuquet, D.J., Gahegan, M., 2003. ICEAGE: interactive clustering and exploration of large and highdimensional geodata. GeoInformatica, 7(3): 229–253. https://doi.org/10.1023/A:1025101015202
Hinneburg, A., Keim, D.A., 1999. Optimal grid-clustering: towards breaking the curse of dimensionality in highdimensional clustering. Proc. 25th Int. Conf. on Very Large Data Bases, p.506–517.
Jones, C.B., Purves, R., Ruas, A., et al., 2002. Spatial information retrieval and geographical ontologies an overview of the SPIRIT project. Proc. 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.387–388. https://doi.org/10.1145/564437.564457
Joshi, T., Joy, J., Kellner, T., et al., 2008. Crosslingual location search. Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.211–218. https://doi.org/10.1145/1390334.1390372
Khodaei, A., Shahabi, C., Li, C., 2010. Hybrid indexing and seamless ranking of spatial and textual features of web documents. LNCS, 6261: 450–466. https://doi.org/10.1007/978-3-642-15364-8_37
Komusiewicz, C., Sorge, M., 2012. Finding dense subgraphs of sparse graphs. Proc. 7th Int. Conf. on Parameterized and Exact Computation, p.242–251. https://doi.org/10.1007/978-3-642-33293-7_23
Lee, D.T., 1982. On k-nearest neighbor Voronoi diagrams in the plane. IEEE Trans. Comput., 100(6): 478–487. https://doi.org/10.1109/tc.1982.1676031
Leung, K.W.T., Lee, D.L., Lee, W.C., 2011. CLR: a collaborative location recommendation framework based on co-clustering. Proc. 34th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.305–314. https://doi.org/10.1145/2009916.2009960
Li, Z.S., Lee, K.C., Zheng, B.H., et al., 2011. IR-tree: an efficient index for geographic document search. IEEE Trans. Knowl. Data Eng., 23(4): 585–599. https://doi.org/10.1109/tkde.2010.149
Mai, H.T., Kim, J., Roh, Y.J., et al., 2013. STHist-C: a highly accurate cluster-based histogram for two and three dimensional geographic data points. GeoInformatica, 17(2): 325–352. https://doi.org/10.1007/s10707-012-0154-y
Ortega, E., Otera, I., Mancebo, S., 2014. TITIM GIS-tool: a GIS-based decision support system for measuring the territorial impact of transport infrastructures. Exp. Syst. Appl., 41(16): 7641–7652. https://doi.org/10.1016/j.eswa.2014.05.028
Saoussen, K., Sami, F., Takwa, T., et al., 2014. Tabu-based GIS for solving the vehicle routing problem. Exp. Syst. Appl., 41(14): 6483–6493. https://doi.org/10.1016/j.eswa.2014.03.028
Schikuta, E., 1996. Grid-clustering: an efficient hierarchical clustering method for very large data sets. Proc. 13th Int. Conf. on Pattern Recognition, p.101–105. https://doi.org/10.1109/icpr.1996.546732
Shamos, M.I., Hoey, D., 1975. Closest-point problems. 16th Annual Symp. on Foundations of Computer Science, p.151–162. https://doi.org/10.1109/sfcs.1975.8
Son, L.H., 2014. Optimizing municipal solid waste collection using chaotic particle swarm optimization in GIS based environments: a case study at Danang city, Vietnam. Exp. Syst. Appl., 41(18): 8062–8074. https://doi.org/10.1016/j.eswa.2014.07.020
Thomee, B., Rae, A., 2013. Uncovering locally characterizing regions within geotagged data. Proc. 22nd Int. Conf. on World Wide Web, p.1285–1296. https://doi.org/10.1145/2488388.2488500
Vaid, S., Jones, C.B., Joho, H., et al., 2005. Spatio-textual indexing for geographical search on the web. Advances in Spatial and Temporal Databases, p.218–235. https://doi.org/10.1007/11535331_13
Wei, L.Y., Zheng, Y., Peng, W.C., 2012. Constructing popular routes from uncertain trajectories. Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.195–203. https://doi.org/10.1145/2339530.2339562
Wu, D.M., Yiu, M.L., Cong, G., et al., 2012. Joint top-k spatial keyword query processing. IEEE Trans. Knowl. Data Eng., 24(10): 1889–1903. https://doi.org/10.1109/icde.2011.5767861
Yuan, J., Zheng, Y., Xie, X., 2012. Discovering regions of different functions in a city using human mobility and POIs. Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, p.186–194. https://doi.org/10.1145/2339530.2339561
Zhang, F.Z., Wilkie, D., Zheng, Y., et al., 2013a. Sensing the pulse of urban refueling behavior. Proc. ACM Int. Joint Conf. on Pervasive and Ubiquitous Computing, p.13–22. https://doi.org/10.1145/2493432.2493448
Zhang, Q., Kang, J.H., Gong, Y.Y., et al., 2013b. Map search via a factor graph model. Proc. 22nd ACM Int. Conf. on Information and Knowledge Management, p.69–78. https://doi.org/10.1145/2505515.2505674
Zhou, Y.H., Xie, X., Wang, C., et al., 2005. Hybrid index structures for location-based web search. Proc. 14th ACM Int. Conf. on Information and Knowledge Management, p.155–162. https://doi.org/10.1145/1099554.1099584
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the Zhejiang Provincial Natural Science Foundation of China (No. LZ13F020001), the National Natural Science Foundation of China (Nos. 61173185 and 61173186), the National Key Technology R&D Program of China (No. 2012BAI34B01), and the Hangzhou S&T Development Plan (No. 20150834M22)
Rights and permissions
About this article
Cite this article
Yu, Z., Wang, C., Bu, Jj. et al. Finding map regions with high density of query keywords. Frontiers Inf Technol Electronic Eng 18, 1543–1555 (2017). https://doi.org/10.1631/FITEE.1600043
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1600043
Keywords
- Map search
- Region search
- Region recommendation
- Spatial keyword search
- Geographic information system
- Location-based service