A parametric approximation algorithm for spatial group keyword queries

Article type: Research Article

Authors: Li, Jincao | Xu, Ming^*

Affiliations: Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China

Correspondence: [*] Corresponding author: Ming Xu, Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China. E-mail: [email protected].

Abstract: With the application of big data, various queries arise for information retrieval. Spatial group keyword queries aim to find a set of spatial objects that cover the query keywords and minimize a goal function such as the total distance between the objects and the query point. This problem is widely found in database applications and is known to be NP-hard. Efficient algorithms for solving this problem can only provide approximate solutions, and most of these algorithms achieve a fixed approximation ratio (the upper bound of the ratio of an approximate goal value to the optimal goal value). Thus, to obtain a self-adjusting algorithm, we propose an approximation algorithm for achieving a parametric approximation ratio. The algorithm makes a trade-off between the approximation ratio and time consumption enabling the users to assign arbitrary query accuracy. Additionally, it runs in an on-the-fly manner, making it scalable to large-scale applications. The efficiency and scalability of the algorithm were further validated using benchmark datasets.

Keywords: Information retrieval, spatial group keyword queries, parametric approximation, database

DOI: 10.3233/IDA-195071

Journal: Intelligent Data Analysis, vol. 25, no. 2, pp. 305-319, 2021