Abstract
In this paper, we propose Geo-Social Keyword (GSK) search, which enables the retrieval of users, points of interest (POIs), or keywords that satisfy geographic, social, and/or textual criteria. We first introduce a general GSK framework that covers a wide range of real-world tasks, including advertisement, context-based search, and market analysis. Then, we present three concrete GSK queries: (i) NPRU that returns the top-k users based on their spatial proximity to a given query location, their popularity, and their similarity to an input set of terms; (ii) NSTP that outputs the top-k POIs based on their proximity to a user v, the number of check-ins by friends of v, and their similarity to a set of terms; (iii) FSKR that discovers the top-k keywords based on their frequency in pairs of friends located within a spatial area. For each query, we develop a processing algorithm that utilizes a novel hybrid index. Finally, we evaluate our framework with thorough experiments using real datasets.
R. Ahuja—Supported by GRF grant 617412 from Hong Kong RGC.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
F should satisfy the condition \(\forall o, o': f_g(o)\ge f_g(o') \wedge f_s(o)\ge f_s(o') \wedge f_t(o)\ge f_t(o') \Rightarrow F(o)\ge F(o')\).
- 2.
A bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set [6].
- 3.
Additional constraints in this case could restrict the top-k POIs to be in a certain area, or enforce certain properties (e.g., restaurant must be open after 10 pm).
References
Facebook ads, audience targeting. https://www.facebook.com/help/229438340403916
Google mobile maps. http://www.google.com/mobile/maps/
GroupOn Now! deals available on Foursquare. https://blog.groupon.com/cities/groupon-now-deals-available-in-foursquare/
Yelp academic dataset. http://www.yelp.com/dataset_challenge/
Armenatzoglou, N., Papadopoulos, S., Papadias, D.: A general framework for geo-social query processing. Proc. VLDB Endow. 6(10), 913–924 (2013)
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. of ACM 13(7), 422–426 (1970)
Cao, X., Cong, G., Jensen, C.S., Ooi, B.C.: Collective spatial keyword querying. In: SIGMOD (2011)
Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. Proc. VLDB Endow. 6(3), 217–228 (2013)
Chen, Y.-Y., Suel, T., Markowetz, A.: Efficient query processing in geographic web search engines. In: SIGMOD (2006)
Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. Proc. VLDB Endow. 2(1), 337–348 (2009)
De Felipe, I., Hristidis, V., Rishe, N.: Keyword search on spatial databases. In: ICDE (2008)
Kalashnikov, D.V., Prabhakar, S., Hambrusch, S.E.: Main memory evaluation of monitoring queries over moving objects. Distrib. Parallel Databases 15(2), 117–135 (2004)
Kargar, M., An, A.: Discovering top-k teams of experts with/without a leader in social networks. In: CIKM (2011)
Kargar, M., An, A.: Keyword search in graphs: finding r-cliques. Proc. VLDB Endow. 4(10), 681–692 (2011)
Khodaei, A., Shahabi, C., Li, C.: Hybrid indexing and seamless ranking of spatial and textual features of web documents. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010, Part I. LNCS, vol. 6261, pp. 450–466. Springer, Heidelberg (2010)
Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: SIGKDD (2009)
Liu, W., Sun, W., Chen, C., Huang, Y., Jing, Y., Chen, K.: Circle of friend query in geo-social networks. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012, Part II. LNCS, vol. 7239, pp. 126–137. Springer, Heidelberg (2012)
Long, C., Wong, R.C.-W., Wang, K., Fu, A.W.-C.: Collective spatial keyword queries: a distance owner-driven approach. In: SIGMOD (2013)
Mouratidis, K., Li, J., Tang, Y., Mamoulis, N.: Joint search by social and spatial proximity. IEEE Trans. Knowl. Data Eng. 10(16), 1169–1184 (2015)
Vaid, S., Jones, C.B., Joho, H., Sanderson, M.: Spatio-textual Indexing for geographical search on the web. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 218–235. Springer, Heidelberg (2005)
Otto, A., Kaulfersch, E., Brinkfeldt, K., Neumaier, K., Zschieschang, O., Andersson, D., Rzepka, S.: Reliability of new SiC BJT power modules for fully electric vehicles. In: Fischer-Wolfarth, J., Meyer, G. (eds.) Advanced Microsystems for Automotive Applications 2014. LNMOB, vol. 1, pp. 235–244. Springer, Heidelberg (2014)
Yang, D.-N., Shen, C.-Y. Lee, W.-C., Chen, M.-S.: On socio-spatial group query for location-based social networks. In: KDD (2012)
Zhang, D., Chee, Y.M., Mondal, A., Tung, A., Kitsuregawa, M.: Keyword search in spatial databases: towards searching by document. In: ICDE (2009)
Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma. W.-Y.: Hybrid index structures for location-based web search. In: CIKM (2005)
Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Comput. Surv. 38(2), 6.1–6.56 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ahuja, R., Armenatzoglou, N., Papadias, D., Fakas, G.J. (2015). Geo-Social Keyword Search. In: Claramunt, C., et al. Advances in Spatial and Temporal Databases. SSTD 2015. Lecture Notes in Computer Science(), vol 9239. Springer, Cham. https://doi.org/10.1007/978-3-319-22363-6_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-22363-6_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22362-9
Online ISBN: 978-3-319-22363-6
eBook Packages: Computer ScienceComputer Science (R0)