2 research outputs found
The Flexible Group Spatial Keyword Query
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.Comment: 12 page
Aggregate Keyword Routing in Spatial Database
Due to the proliferation of Location-Based Service and popularity of online geo-tagged web pages, spatial keyword search has attracted significant attention from both academic and industrial communities. In this paper, we study the problem of finding the nearest aggregate point from multiple query points travelling through a set objects described by a given set of keywords, as well as the optimal routes from the query points to the aggregate point. This problem is defined as the Aggregate Keyword Routing (AKR) Query. We devise an exact algorithm to AKR query based on ellipse pruning. Next we propose an efficient approximate algorithm for AKR: Center Based Assignment (CBA). The performance of the proposed algorithms are evaluated with real data, the results demonstrate the efficiency and the effectiveness