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Influential facilities placement over moving objects

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Abstract

In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. We show that this problem is NP-hard and present a basic hill-climb algorithm, namely GreedyP. We prove this method with \( (1- \frac{1}{e}) \) approximation ratio. One core challenge is to identify and reduce the overlap of the influence from different selected locations to maximize the marginal benefits. Therefore, the GreedyP approach may be very costly when the number of moving objects is large. In order to address the problem, we also propose another GreedyPS algorithm based on FM-sketch technique, which maps the moving objects to bitmaps such that the marginal benefit can be easily observed through bit-wise operations. Through this way, we are able to save more than a half running time while preserving the result quality. We further present a pair of extensions to the problem, namely k-Additional and k-Eliminative Influential Facility Placement problems. We also present corresponding approximate solutions towards both extensions and theoretically show that results of both algorithms are guaranteed. Experiments on real datasets verify the efficiency and effectiveness for all these algorithms comparing with baselines.

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Notes

  1. In the following of this paper, we shall use user and object interchangeably for ease of presentation.

  2. In fact, FM sketch contains a series of other techniques, we only employ the hash strategy herein.

  3. https://www.hotelmanagement.net/franchising/marriott-announces-7-new-hotels-across-3-brands-for-china.

  4. https://www.dallasnews.com/business/local-companies/2017/12/07/exxon-to-open-eight-mobil-gas-stations-in-mexico-with-plans-for-50-by-early-2018/.

  5. https://lihuixidian.github.io/malos/.

References

  1. Abdullatif, A., Masulli, F., Rovetta, S.: Tracking time evolving data streams for short-term traffic forecasting. Data Sci. Eng. 2(3), 210–223 (2017)

    Article  Google Scholar 

  2. Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: The million follower fallacy. In: Cohen WW, Gosling S (eds) Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM 2010, Washington, DC, USA, May 23–26, 2010, The AAAI Press (2010)

  3. Chen, L., Gao, Y., Fang, Z., Miao, X., Jensen, C.S., Guo, C.: Real-time distributed co-movement pattern detection on streaming trajectories. Proc. VLDB Endow. 12(10), 1208–1220 (2019)

    Article  Google Scholar 

  4. Feige, U.: A threshold of ln n for approximating set cover. J. ACM 45(4), 634–652 (1998)

    Article  MathSciNet  Google Scholar 

  5. Feng, S., Cong, G., Khan, A., Li, X., Liu, Y., Chee, Y.M.: Inf2vec: Latent representation model for social influence embedding. In: 34th IEEE International Conference on Data Engineering, ICDE 2018, Paris, France, April 16–19, 2018, IEEE Computer Society, pp. 941–952 (2018)

  6. Flajolet, P., Martin, G.N.: Probabilistic counting algorithms for data base applications. J. Comput. Syst. Sci. 31(2), 182–209 (1985)

    Article  MathSciNet  Google Scholar 

  7. Gao, Y., Zheng, B., Chen, G., Li, Q., Chen, C., Chen, G.: Efficient mutual nearest neighbor query processing for moving object trajectories. Inf. Sci. 180(11), 2176–2195 (2010)

    Article  Google Scholar 

  8. Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.: Influence maximization in trajectory databases. IEEE Trans. Knowl. Data Eng. 29(3), 627–641 (2017)

    Article  Google Scholar 

  9. Hajian, B., White, T.: Modelling influence in a social network: Metrics and evaluation. In: PASSAT/SocialCom 2011, Privacy, Security, Risk and Trust (PASSAT), 2011 IEEE Third International Conference on and 2011 IEEE Third International Conference on Social Computing (SocialCom), Boston, MA, USA, 9–11 October 2011, IEEE Computer Society, pp. 497–500 (2011)

  10. Huang, W., Yu, J.X.: Investigating TSP heuristics for location-based services. Data Sci. Eng. 2(1), 71–93 (2017)

    Article  Google Scholar 

  11. Jabeur, L.B., Tamine, L., Boughanem, M.: Active microbloggers: Identifying influencers, leaders and discussers in microblogging networks. In: Calderón-Benavides L, González-Caro CN, Chávez E, Ziviani N (eds) String Processing and Information Retrieval-19th International Symposium, SPIRE 2012, Cartagena de Indias, Colombia, October 21–25, 2012. Proceedings, Springer, Lecture Notes in Computer Science, vol. 7608, pp. 111–117 (2012)

  12. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24–27, 2003, pp. 137–146 (2003)

  13. Korn, F., Muthukrishnan, S.: Influence sets based on reverse nearest neighbor queries. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 16–18, 2000, Dallas, Texas, USA, pp. 201–212 (2000)

  14. Kritter, J., Brévilliers, M., Lepagnot, J., Idoumghar, L.: On the optimal placement of cameras for surveillance and the underlying set cover problem. Appl. Soft. Comput. 74, 133–153 (2019)

    Article  Google Scholar 

  15. Lampos, V., Aletras, N., Preotiuc-Pietro, D., Cohn, T.: Predicting and characterising user impact on twitter. In: Bouma G., Parmentier Y., (eds) Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014, April 26–30, 2014, Gothenburg, Sweden, The Association for Computer Linguistics, pp. 405–413. https://doi.org/10.3115/v1/e14-1043 (2014)

  16. Li, D., Li, H., Wang, M., Cui, J.: k-collective influential facility placement over moving object. In: 20th IEEE International Conference on Mobile Data Management, MDM 2019, Hong Kong, SAR, China, June 10–13, 2019, pp. 191–200 (2019)

  17. Li, G., Chen, S., Feng, J., Tan, K., Li, W.: Efficient location-aware influence maximization. In: International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, June 22–27, 2014, pp. 87–98 (2014)

  18. Li, Y., Bao, J., Li, Y., Wu, Y., Gong, Z., Zheng, Y.: Mining the most influential k-location set from massive trajectories. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2016, Burlingame, California, USA, October 31–November 3, 2016, pp. 51:1–51:4 (2016)

  19. Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: The k most representative skyline operator. In: Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15–20, 2007, pp. 86–95 (2007)

  20. Mitra, S.: Identifying top-k optimal locations for placement of large-scale trajectory-aware services. In: Proceedings of the VLDB 2016 PhD Workshop Co-located with the 42nd International Conference on Very Large Databases (VLDB 2016), New Delhi, India, September 9, 2016 (2016)

  21. Mitra, S., Saraf, P., Bhattacharya, A.: TIPS: mining top-k locations to minimize user-inconvenience for trajectory-aware services. CoRR arXiv:1709.02343 (2017a)

  22. Mitra, S., Saraf, P., Sharma, R., Bhattacharya, A., Ranu, S., Bhandari, H.: Netclus: a scalable framework for locating top-k sites for placement of trajectory-aware services. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, April 19–22, 2017, pp 87–90 (2017b)

  23. Peng, S., Yang, A., Cao, L., Yu, S., Xie, D.: Social influence modeling using information theory in mobile social networks. Inf. Sci. 379, 146–159 (2017)

    Article  Google Scholar 

  24. Sun, Y., Huang, J., Chen, Y., Zhang, R., Du, X.: Location selection for utility maximization with capacity constraints. In: 21st ACM International Conference on Information and Knowledge Management, CIKM’12, Maui, HI, USA, October 29–November 02, 2012, pp. 2154–2158 (2012)

  25. Wang, M., Li, H., Cui, J., Deng, K., Bhowmick, S.S., Dong, Z.: PINOCCHIO: probabilistic influence-based location selection over moving objects. In: 33rd IEEE International Conference on Data Engineering, pp. 21–22 (2017)

  26. Wang, S., Bao, Z., Culpepper, J.S., Sellis, T., Cong, G.: Reverse k nearest neighbor search over trajectories. IEEE Trans. Knowl. Data Eng. 30(4), 757–771 (2018)

    Article  Google Scholar 

  27. Wong, R.C., Özsu, M.T., Yu, P.S., Fu, A.W., Liu, L.: Efficient method for maximizing bichromatic reverse nearest neighbor. PVLDB 2(1), 1126–1137 (2009)

    Google Scholar 

  28. Wu, T., Lin, J.: Solving the competitive discretionary service facility location problem. Eur. J. Oper. Res. 144(2), 366–378 (2003)

    Article  Google Scholar 

  29. Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30–September 2, 2005, pp. 946–957 (2005)

  30. Xu, C., Gu, Y., Zimmermann, R., Lin, S., Yu, G.: Group location selection queries over uncertain objects. IEEE Trans. Knowl. Data Eng. 25(12), 2796–2808 (2013)

    Article  Google Scholar 

  31. Yan, D., Wong, R.C., Ng, W.: Efficient methods for finding influential locations with adaptive grids. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, UK, October 24–28, 2011, pp. 1475–1484 (2011)

  32. Yiu, M.L., Dai, X., Mamoulis, N., Vaitis, M.: Top-k spatial preference queries. In: Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15–20, 2007, pp. 1076–1085 (2007)

  33. Zhang, D., Guo, L., Nie, L., Shao, J., Wu, S., Shen, H.T.: Targeted advertising in public transportation systems with quantitative evaluation. ACM Trans. Inf. Syst. 35(3), 1–20 (2017)

    Google Scholar 

  34. Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19–23, 2018, pp. 2748–2757 (2018a)

  35. Zhang, X., Rey, D., Waller, S.T.: Multitype recharge facility location for electric vehicles. Comp. Aid. Civ. Infrastruct Eng. 33(11), 943–965 (2018b)

    Article  Google Scholar 

  36. Zhou, Z., Wu, W., Li, X., Lee, M., Hsu, W.: Maxfirst for maxbrknn. In: Proceedings of the 27th International Conference on Data Engineering, ICDE 2011, April 11–16, 2011, Hannover, Germany, pp. 828–839 (2011)

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Funding

It is supported by National Natural Science Foundation of China (No. 61672408, 61972309, 61976168), CCF-Huawei Database System Innovation Research Plan (No. 2020010B), Natural Science Basic Research Program of Shaanxi (No. 2020JM-575) and China 111 Project (No. B16037).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

    Ying Zhou, Hui Li & Dan Li

  2. School of Cyber Engineering, Xidian University, Xi’an, China

    Ying Zhou & Hui Li

  3. School of Computer Science, Xi’an Polytechnic University, Xi’an, China

    Meng Wang

  4. School of Computer Science and Technology, Xidian University, Xi’an, China

    Jiangtao Cui

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  1. Ying Zhou
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  2. Hui Li
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Correspondence to Hui Li.

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A short version of this paper appeared in [16], which received the Best Paper Award Runner-up in MDM 2019.

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Zhou, Y., Li, H., Li, D. et al. Influential facilities placement over moving objects. Distrib Parallel Databases 39, 607–636 (2021). https://doi.org/10.1007/s10619-020-07311-0

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