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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/.

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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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