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
N1 - Publisher Copyright:
© 2021 IEEE.
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.
UR - http://www.scopus.com/inward/record.url?scp=85112867928&partnerID=8YFLogxK
U2 - 10.1109/ICDE51399.2021.00253
DO - 10.1109/ICDE51399.2021.00253
M3 - Conference contribution
AN - SCOPUS:85112867928
SN - 9781728191850
T3 - Proceedings - International Conference on Data Engineering
SP - 2334
EP - 2335
BT - Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PB - IEEE Computer Society
Y2 - 19 April 2021 through 22 April 2021
ER -