Processing Multiple-User Location-Based Keyword Queries

Abstract

Geosocial networking allows users to interact with respect to their current locations, which enables a group of users to determine where to meet. This calls for techniques that support processing of Multiple-user Location-based Keyword (MULK) queries, which return a set of Point-of-Interests (POIs) that are 'close' to the locations of the users in a group and can provide them with potential options at the lowest expense (e.g., minimizing travel distance). In this paper, we formalize the MULK query and propose a dynamic programming-based algorithm to find the optimal result set. Further, we design two approximation algorithms to improve MULK query processing efficiency. The experimental evaluations show that our solutions are feasible and efficient under various parameter settings.

A Trie-Based Authentication Scheme for Approximate String Queries

“…In [9]- [11], Location-based spatial query verification schemes have been proposed, which effectively solve the problem of result verification for nearest neighbor, k-nearest neighbor and skyline query, respectively. In [12]- [14], the authors proposed a verification scheme to solve the problem of verifying the results of spatial multi-user queries. Multiple SQL query verification schemes are described in [3].…”

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

“…In order to achieve this goal, the algorithm first used Tr-tree spatial index to improve the query efficiency and then introduced the community satisfaction degree model based on the knowledge graph to comprehensively evaluate whether POIs can better meet the preference of the user community. Wang et al [14] proposed an algorithm to find the optimal result of multiuser positional keyword queries based on the minimum cost function. Wang et al [15] Saad N H M et al proposed the SkyQUD algorithm, which divided the data set according to the characteristics of each query object, and in the pruning process, it used a probability threshold τ to limit the possibility of a certain dimension in the data set to become the user's preference, but the query accuracy of this method was low.…”