MaxiZone: Maximizing influence zone over geo-textual data (extended abstract)

Qing Liu; Ziyuan Zhu; Jianliang Xu; Yunjun Gao

doi:10.1109/ICDE51399.2021.00253

MaxiZone: Maximizing influence zone over geo-textual data (extended abstract)

Qing Liu, Ziyuan Zhu, Jianliang Xu, Yunjun Gao

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

A reverse top-k keyword-based location query returns the influence zone for the query object. Given a specified query object q, the influence zone of q varies for different key-word sets. Users may be interested in identifying the maximum influence zone of the query object. To this end, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. The MaxiZone problem has many real-life applications, e.g., a business owner would like to identify the maximum influence zone so as to attract as many customers as possible. To address the MaxiZone problem, we propose three algorithms, including a basic algorithm, an index-centric algorithm together with a series of optimizations and a sampling-based algorithm. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of proposed algorithms.

Original language	English
Title of host publication	Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
Publisher	IEEE Computer Society
Pages	2334-2335
Number of pages	2
ISBN (Electronic)	9781728191843
ISBN (Print)	9781728191850
DOIs	https://doi.org/10.1109/ICDE51399.2021.00253
Publication status	Published - Apr 2021
Event	37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece Duration: 19 Apr 2021 → 22 Apr 2021 https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2021-April
ISSN (Print)	1084-4627
ISSN (Electronic)	2375-0286

Conference

Conference	37th IEEE International Conference on Data Engineering, ICDE 2021
Country/Territory	Greece
City	Virtual, Chania
Period	19/04/21 → 22/04/21
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding

Scopus Subject Areas

Software
Signal Processing
Information Systems

Access to Document

10.1109/ICDE51399.2021.00253

Cite this

Liu, Q., Zhu, Z., Xu, J., & Gao, Y. (2021). MaxiZone: Maximizing influence zone over geo-textual data (extended abstract). In Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021 (pp. 2334-2335). (Proceedings - International Conference on Data Engineering; Vol. 2021-April). IEEE Computer Society. https://doi.org/10.1109/ICDE51399.2021.00253

@inproceedings{d45d13a2649a4e5a8307df5637b504e3,

title = "MaxiZone: Maximizing influence zone over geo-textual data (extended abstract)",

abstract = "A reverse top-k keyword-based location query returns the influence zone for the query object. Given a specified query object q, the influence zone of q varies for different key-word sets. Users may be interested in identifying the maximum influence zone of the query object. To this end, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. The MaxiZone problem has many real-life applications, e.g., a business owner would like to identify the maximum influence zone so as to attract as many customers as possible. To address the MaxiZone problem, we propose three algorithms, including a basic algorithm, an index-centric algorithm together with a series of optimizations and a sampling-based algorithm. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of proposed algorithms.",

author = "Qing Liu and Ziyuan Zhu and Jianliang Xu and Yunjun Gao",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 37th IEEE International Conference on Data Engineering, ICDE 2021 ; Conference date: 19-04-2021 Through 22-04-2021",

year = "2021",

month = apr,

doi = "10.1109/ICDE51399.2021.00253",

language = "English",

isbn = "9781728191850",

series = "Proceedings - International Conference on Data Engineering",

publisher = "IEEE Computer Society",

pages = "2334--2335",

booktitle = "Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding",

}

Liu, Q, Zhu, Z, Xu, J & Gao, Y 2021, MaxiZone: Maximizing influence zone over geo-textual data (extended abstract). in Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021. Proceedings - International Conference on Data Engineering, vol. 2021-April, IEEE Computer Society, pp. 2334-2335, 37th IEEE International Conference on Data Engineering, ICDE 2021, Virtual, Chania, Greece, 19/04/21. https://doi.org/10.1109/ICDE51399.2021.00253

MaxiZone: Maximizing influence zone over geo-textual data (extended abstract). / Liu, Qing; Zhu, Ziyuan; Xu, Jianliang et al.
Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021. IEEE Computer Society, 2021. p. 2334-2335 (Proceedings - International Conference on Data Engineering; Vol. 2021-April).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - MaxiZone

T2 - 37th IEEE International Conference on Data Engineering, ICDE 2021

AU - Liu, Qing

AU - Zhu, Ziyuan

AU - Xu, Jianliang

AU - Gao, Yunjun

PY - 2021/4

Y1 - 2021/4

N2 - A reverse top-k keyword-based location query returns the influence zone for the query object. Given a specified query object q, the influence zone of q varies for different key-word sets. Users may be interested in identifying the maximum influence zone of the query object. To this end, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. The MaxiZone problem has many real-life applications, e.g., a business owner would like to identify the maximum influence zone so as to attract as many customers as possible. To address the MaxiZone problem, we propose three algorithms, including a basic algorithm, an index-centric algorithm together with a series of optimizations and a sampling-based algorithm. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of proposed algorithms.

AB - A reverse top-k keyword-based location query returns the influence zone for the query object. Given a specified query object q, the influence zone of q varies for different key-word sets. Users may be interested in identifying the maximum influence zone of the query object. To this end, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. The MaxiZone problem has many real-life applications, e.g., a business owner would like to identify the maximum influence zone so as to attract as many customers as possible. To address the MaxiZone problem, we propose three algorithms, including a basic algorithm, an index-centric algorithm together with a series of optimizations and a sampling-based algorithm. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of proposed algorithms.

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MaxiZone: Maximizing influence zone over geo-textual data (extended abstract)

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