Abstract
The dissemination of messages to a vast number of mobile users has raised a lot of attention. This issue is inherent in emerging applications, such as location-based targeted advertising, selective information disseminating, and ride sharing. In this paper, we examine how to support location-based message dissemination in an effective and efficient manner. Our main idea is to develop a location-aware version of the Pub/Sub model, which was designed for message dissemination. While a lot of studies have successfully used this model to match the interest of subscriptions (e.g., the properties of potential customers) and events (e.g., information of casual users), the issues of incorporating the location information of subscribers and publishers have not been well addressed. We propose to model subscriptions and events by boolean expressions and location data. This allows complex information to be specified. However, since the number of publishers and subscribers can be enormous, the time cost for matching subscriptions and events can be prohibitive. To address this problem, we have developed the \(R^I\)-tree. This data structure is an integration of the R-tree and the dynamic interval-tree. Together with our novel pruning strategy on \(R^I\)-tree, our solution can effectively and efficiently return the top-k subscriptions with respect to an event. We have performed extensive evaluations to verify our approach.
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More generally, the non-spatial similarity can be any monotonic function of the weights.
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References
Chen, L., Cong, G., Jensen, C.S., Wu, D.: Spatial keyword query processing: an experimental evaluation. In: PVLDB’2013, pp. 217–228. VLDB Endowment (2013)
Chen, L., Cong, G., Cao, X., Tan, K.-L.: Temporal spatial-keyword top-k publish/subscribe. In: ICDE 2015, pp. 255–266 (2015)
Cheng, Z., Caverlee, J., Lee, K., Sui, D.Z.: Exploring millions of footprints in location sharing services. In: ICWSM 2011, pp. 81–88 (2011)
Cong, G., Jensen, C.S., Wu, D.: Efficient retrieval of the top-k most relevant spatial web objects. In: VLDB 2009, vol. 2, pp. 337–348 (2009)
de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications. Springer-Verlag, Heidelberg (2000)
Eugster, P.T., Felber, P.A., Guerraoui, R., Kermarrec, A.-M.: The many faces of publish/subscribe. ACM Comput. Surv. 35(2), 114–131 (2003)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS 2001, pp. 102–113. ACM, NY, USA (2001)
Fontoura, M., et al.: Efficiently evaluating complex boolean expressions. In: SIGMOD 2010, pp. 3–14. ACM, NY, USA (2010)
Guo, L., Zhang, D., Li, G., Tan, K.-L., Bao, Z.: Location-aware pub/sub system: When continuous moving queries meet dynamic event streams. In: SIGMOD 2015, pp. 843-857. ACM (2015)
Guttman. A.: R-trees: a dynamic index structure for spatial searching. In SIGMOD 1984, pp. 47–57. ACM, NY, USA (1984)
Hu, H., Liu, Y., Li, G., Feng, J., Tan, K.-L.: A location-aware publish/subscribe framework for parameterized spatio-textual subscriptions. In: ICDE 2015, pp. 711–722 (2015)
Kaplan, H., Molad, E., Tarjan, R.E.: Dynamic rectangular intersection with priorities. In: STOC 2003, p. 639. ACM Press, New York, June 2003
Li, G., Wang, Y., Wang, T., Feng, J.: Location-aware publish/subscribe. In: KDD 2013, pp. 802–810. ACM Press, New York (2013)
Machanavajjhala, A., Vee, E., Garofalakis, M., Shanmugasundaram, J.: Scalable ranked publish/subscribe. In: VLDB 2008, vol. 1, issue no. 1, pp. 451–462, August 2008
Mehlhorn, K.: Data structures and algorithms 3: Multi-dimensional Searching and Computational Geometry. Monographs in Theoretical Computer Science. An EATCS Series. Springer, Heidelberg (1984)
Sadoghi, M., Jacobsen, H.-A.: Be-tree: an index structure to efficiently match boolean expressions over high-dimensional discrete space. In: SIGMOD 2011, pp. 637-648. ACM (2011)
Sadoghi, M., Jacobsen, H.-A.: Relevance matters: Capitalizing on less (top-k matching in publish/subscribe). In: ICDE 2012, pp. 786–797 (2012)
Whang, S.E., Garcia-Molina, H., Brower, C. J. Shanmugasundaram, S. Vassilvitskii, E. Vee, and R. Yerneni. Indexing boolean expressions. In: VLDB 2009, vol. 2, issue no. 1, pp. 37–48 (2009)
Yu, M., Li, G., Wang, T., Feng, J., Gong, Z.: Efficient filtering algorithms for location-aware publish/subscribe. IEEE TKDE 27(4), 950–963 (2015)
Zhang, D., Chan, C.-Y., Tan, K.-L.: An efficient publish/subscribe index for e-commerce databases. In: Proceedings VLDB Endow, vol. 7, issue no. 8, pp. 613–624 (2014)
Zhang, D., Tan, K.-L., Tung, A.K.H.: Scalable top-k spatial keyword search. In: EDBT 2013, pp. 359–370. ACM Press, New York, USA (2013)
Acknowledgments
Jiafeng Hu and Reynold Cheng were supported by the Research Grants Council of Hong Kong (RGC Project (HKU 711110)). Dingming Wu was supported by HKU 714712E. Beihong Jin was supported by the National Natural Science Foundation of China under Grant No. 61472408 and the Opening Foundation of Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications.
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Hu, J., Cheng, R., Wu, D., Jin, B. (2015). Efficient Top-k Subscription Matching for Location-Aware Publish/Subscribe. 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_18
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