# The Flexible Group Spatial Keyword Query

## TL;DR

This paper introduces the Flexible Group Spatial Keyword Query, a new service enabling groups to find optimal POIs based on spatial and keyword criteria, with algorithms supporting various subgroup and tradeoff considerations.

## Contribution

It proposes novel algorithms for processing flexible group spatial keyword queries, including subgroup and tradeoff variants, with theoretical bounds and experimental validation.

## Key findings

- Algorithms are effective and efficient in real datasets.
- The approach balances group and subgroup optimization.
- Theoretical bounds support the algorithms' performance.

## Abstract

We present a new class of service for location based social networks, called the Flexible Group Spatial Keyword Query, which enables a group of users to collectively find a point of interest (POI) that optimizes an aggregate cost function combining both spatial distances and keyword similarities. In addition, our query service allows users to consider the tradeoffs between obtaining a sub-optimal solution for the entire group and obtaining an optimimized solution but only for a subgroup. We propose algorithms to process three variants of the query: (i) the group nearest neighbor with keywords query, which finds a POI that optimizes the aggregate cost function for the whole group of size n, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup and a POI that optimizes the aggregate cost function for a given subgroup size m (m <= n), and (iii) the multiple subgroup nearest neighbor with keywords query, which finds optimal subgroups and corresponding POIs for each of the subgroup sizes in the range [m, n]. We design query processing algorithms based on branch-and-bound and best-first paradigms. Finally, we provide theoretical bounds and conduct extensive experiments with two real datasets which verify the effectiveness and efficiency of the proposed algorithms.