Spatial Textual Top-k Search in Mobile Peer-to-Peer Networks
Mobile hardware and software is quickly becoming the dominant computing model for technologically savvy people around the world. Nowadays, mobile devices are commonly equipped with GPS and wireless connections. Users have also developed the habit of regularly checking into a location, and adding comments or ratings for restaurants or any place of interest visited. This work explores new approaches to make data available from a local network, and to build a collaborative search application that can suggest locations of interest based on distance, user reviews and ratings. The proposed system includes light-weight indexing to support distributed search over spatio-textual data on mobile devices, and a ranking function to score objects of interest with relevant user review content. From our experimental study using a Yelp dataset, we found that our proposed system provides substantial efficiency gains when compared with a centralised system, with little loss in overall effectiveness. We also present a methodology to quantify efficiency and effectiveness trade-offs in decentralized search systems using the Rank-based overlap (RBO) measure.
This work was supported by the Australian Research Council’s Discovery Projects Scheme (DP140101587). Shane Culpepper is the recipient of an Australian Research Council DECRA Research Fellowship (DE140100275).
- 2.Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. PVLDB 6(3), 217–228 (2013)Google Scholar
- 3.Choudhury, F.M., Culpepper, J.S., Sellis, T., Cao, X.: Maximizing bichromatic reverse spatial and textual k nearest neighbor queries. PVLDB 9(6), 456–467 (2016)Google Scholar
- 6.Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., Suel, T.: Text vs. space: efficient geo-search query processing. In: Proceedings of CIKM, pp. 423–432 (2011)Google Scholar
- 7.Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. PVLDB 2(1), 337–348 (2009)Google Scholar
- 8.Demirbas, M., Ferhatosmanoglu, H.: Peer-to-peer spatial queries in sensor networks. In: Proceedings of P2P, pp. 32–39 (2003)Google Scholar
- 10.Köpke, A., Swigulski, M., Wessel, K., Willkomm, D., Haneveld, P.T.K., Parker, T.E.V., Visser, O.W., Lichte, H.S., Valentin, S.: Simulating wireless, mobile networks in OMNeT++ the MiXiM vision. In: Proceedings of STTCNS (2008)Google Scholar
- 12.Li, Y., Chen, H., Xie, R., Wang, J.Z.: Bgn: a novel scatternet formation algorithm for bluetooth-based sensor networks. Mob. Inf. Syst. 7, 93–106 (2011)Google Scholar
- 13.Li, Z., Lee, K.C.K., Zheng, B., Lee, W.-C., Lee, D., Wang, X.: IR-Tree: an efficient index for geographic document search. TKDE 23(4), 585–599 (2011)Google Scholar
- 14.Mackenzie, J., Choudhury, F.M., Culpepper, J.S.: Efficient location-aware web search. In: Proceedings of ADCS, pp. 4: 1–4: 8 (2015)Google Scholar
- 16.Nghiem, T.P., Maulana, K., Green, D., Waluyo, A.B., Taniar, D.: Peer-to-peer bichromatic reverse nearest neighbors in mobile ad-hoc networks. JPDC 74(11), 3128–3140 (2013)Google Scholar
- 19.Zhang, D., Chan, C.-Y., Tan, K.-L.: Processing spatial keyword query as a top-k aggregation query. In: Proceedings of SIGIR (2014)Google Scholar
- 20.Zhou, Y., Xie, X., Wang, C., Gong, Y., Ma, W.-Y.: Hybrid index structures for location-based web search. In: Proceedings of CIKM (2005)Google Scholar