MaxiZone: Maximizing Influence Zone over Geo-Textual Data

Given a geo-textual dataset $\mathbb{O}$ , a set $\varphi$ of keywords, a query object $q \in \mathbb{O}$ , and an integer $k$ , a reverse top-k keyword based location query returns the influence zone R of q such that q belongs to the result of a top-k spatial keyword query with query keywords $\varphi$ and any location in $\mathcal{R}$ as arguments. For a query object q, the influence zone of q varies for different keywords $\varphi$ . Users may be interested in identifying the maximum influence zone of the query object. In this paper, we study the problem called MaxiZone that finds the keyword set maximizing the influence zone of a specified query object. A straightforward way is to compute the influence zone for every candidate keyword set. Obviously, this is infeasible if there are a large number of candidate keyword sets. We propose a more efficient index-centric algorithm together with a series of optimizations as well as a sampling-based algorithm, to facilitate the query processing. Moreover, we extend the proposed algorithms to address an interesting variant of MaxiZone problem. Extensive empirical study using real-world datasets demonstrates the effectiveness and efficiency of our proposed algorithms.