Given the movement history (e.g., historical trajactory/check-in logs) for massive users, how to find a location, from a group of candidates, to place some facility, such that the new facility can probably cover the most number of moving users. We present a novel model, namely PINOCCHIO, that can address the aforementioned problem efficiently. The results of this work have been published in the following papers, please cite the following appropriately if you are using the code for PINOCCHIO.
Github »Given the movement history (e.g., historical trajactory/check-in logs) for massive users, how to find a location to deploy the next facility, while there are k existing ones, such that the aggregated utility for the facility network is maximized. We present a novel model, namely MILE-RUN, that can address the aforementioned problem efficiently. The results of this work have been published in the following papers, please cite the following appropriately if you are using the code for MILE-RUN.
Github »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. The results of this work have been published in the following papers, please cite the following appropriately if you are using the code for k-CollectiveFP.
Github »Facility relocation (FR) problem, which aims to optimize the placement of facilities to accommodate the changes of users’ locations, has a broad spectrum of applications. Despite the significant progress made by existing solutions to the FR problem, they all assume each user is stationary and represented as a single point. Unfortunately, in reality, objects (e.g., people, animals) are mobile. For example, a car-sharing user picks up a vehicle from a station close to where she is currently located. Consequently, these efforts may fail to identify superior solution to the FR problem. In this paper, for the first time, we take into account movement history of users and introduce a novel FR problem, called motion-fr, to address the above limitation.
Github »@article{PINO_TKDE16, author = {Meng Wang and Hui Li and Jiangtao Cui and Ke Deng and Sourav S. Bhowmick and Zhenhua Dong}, title = {{PINOCCHIO:} Probabilistic Influence-Based Location Selection over Moving Objects}, journal = {{IEEE} Trans. Knowl. Data Eng.}, volume = {28}, number = {11}, pages = {3068--3082}, year = {2016}, url = {https://doi.org/10.1109/TKDE.2016.2580138}, doi = {10.1109/TKDE.2016.2580138}, }
@inproceedings{PINO_ICDE17, author = {Meng Wang and Hui Li and Jiangtao Cui and Ke Deng and Sourav S. Bhowmick and Zhenhua Dong}, title = {{PINOCCHIO:} Probabilistic Influence-Based Location Selection over Moving Objects}, booktitle = {33rd {IEEE} International Conference on Data Engineering, {ICDE} 2017, San Diego, CA, USA, April 19-22, 2017}, pages = {21--22}, year = {2017}, url = {https://doi.org/10.1109/ICDE.2017.17}, doi = {10.1109/ICDE.2017.17}, } }
@article{DBLP:journals/isci/CuiWLC18, author = {Jiangtao Cui and Meng Wang and Hui Li and Yang Cai}, title = {Place Your Next Branch with {MILE-RUN:} Min-dist Location Selection over User Movement}, journal = {Inf. Sci.}, volume = {463-464}, pages = {1--20}, year = {2018}, url = {https://doi.org/10.1016/j.ins.2018.06.036}, doi = {10.1016/j.ins.2018.06.036}, timestamp = {Fri, 26 Oct 2018 12:57:07 +0200}, biburl = {https://dblp.org/rec/bib/journals/isci/CuiWLC18}, bibsource = {dblp computer science bibliography, https://dblp.org} }
@inproceedings{DBLP:conf/mdm/Li00C19, author = {Dan Li and Hui Li and Meng Wang and Jiangtao Cui}, title = {k-Collective Influential Facility Placement Over Moving Object}, booktitle = {20th {IEEE} International Conference on Mobile Data Management, {MDM} 2019, Hong Kong, SAR, China, June 10-13, 2019}, pages = {191--200}, year = {2019}, }
@article{10.1145/3361740, author = {Wang, Meng and Li, Hui and Cui, Jiangtao and Bhowmick, Sourav S. and Liu, Ping}, title = {FROST: Movement History–Conscious Facility Relocation}, year = {2020}, issue_date = {February 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {11}, number = {1}, issn = {2157-6904}, url = {https://doi.org/10.1145/3361740}, doi = {10.1145/3361740}, journal = {ACM Trans. Intell. Syst. Technol.}, month = jan, articleno = {Article 9}, numpages = {26}, keywords = {spatial database, movement history, Facility relocation}, }
@InProceedings{DBLP:conf/edbt/Li20, author = {Di Yang and Hui Li and Dan Li and Meng Wang and Jiangtao Cui}, title = {{MALOS:} A Movement-Aware Location Selection System}, booktitle = {Joint 2020 {EDBT/ICDT} Conferences, {EDBT} '20 Proceedings, Copenhagen, Denmark, March 30-April 2, 2020}, year = {2020}, pages = {}, }
@Article{Zhou2020, author={Zhou, Ying and Li, Hui and Li, Dan and Wang, Meng and Cui, Jiangtao}, title={Influential facilities placement over moving objects}, journal={Distributed and Parallel Databases}, year={2020}, month={Sep}, day={20}, issn={1573-7578}, doi={10.1007/s10619-020-07311-0}, url={https://doi.org/10.1007/s10619-020-07311-0} }
@inproceedings{WH_CIKM21, author = {Hu Wang and Hui Li and Meng Wang and Jiangtao Cui}, title = {Addressing the Hardness of k-Facility Relocation Problem: a Pair of Approximate Solutions}, booktitle = {30th ACM International Conference on Information and Knowledge Management (CIKM), Online, Australia, Nov 1 - 5, 2021}, pages = {}, publisher = {{ACM}}, year = {2021}, }
@article{tist_wh2023, author = {Wang, Hu and Li, Hui and Wang, Meng and Cui, Jiangtao}, title = {Towards Balancing the Efficiency and Effectiveness in k-Facility Relocation Problem}, year = {2023}, issue_date = {February 2023}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {preprint}, number = {}, issn = {}, doi = {}, journal = {ACM Trans. Intell. Syst. Technol.}, articleno = {}, numpages = {24}, }
@article{WM_2023Geo, author = {马毓哲 and 王蒙 and 李辉 and 崔江涛 and 刘俊华 and 李瑞蒙}, title = {RUG：收益驱动的单向共享汽车用户重定位}, journal = {地球信息科学学报}, year = {2023}, volume = {25}, number = {12}, pages = {1-14}, }
@InProceedings{ndbc23, author = {李晨伟 and 王蒙 and 李辉 and 王西汉 and 崔江涛}, title = {CoSTUR：面向用户评级的空间文本竞争选址研究}, booktitle = {The 40th National Database Conference, China (NDBC'2023)}, year = {2023}, }