Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2011
Abstract
Location-based recommendation services recommend objects to the user based on the user’s preferences. In general, the nearest objects are good choices considering their spatial proximity to the user. However, not only the distance of an object to the user but also their directional relationship are important. Motivated by these, we propose a new spatial query, namely a direction-based surrounder (DBS) query, which retrieves the nearest objects around the user from different directions. We define the DBS query not only in a two-dimensional Euclidean space E">EE but also in a road network R">RR . In the Euclidean space E">EE , we consider two objects a and b are directional close w.r.t. a query point q iff the included angle ∠aqb">∠aqb∠aqb is bounded by a threshold specified by the user at the query time. In a road network R">RR , we consider two objects a and b are directional close iff their shortest paths to q overlap. We say object a dominates object b iff they are directional close and meanwhile a is closer to q than b. All the objects that are not dominated by others based on the above dominance relationship constitute direction-based surrounders (DBSs). In this paper, we formalize the DBS query, study it in both the snapshot and continuous settings, and conduct extensive experiments with both real and synthetic datasets to evaluate our proposed algorithms. The experimental results demonstrate that the proposed algorithms can answer DBS queries efficiently.
Keywords
Spatial database, Surrounder query, Location-based recommendation, Direction
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
VLDB Journal
Volume
20
Issue
5
First Page
743
Last Page
766
ISSN
1066-8888
Identifier
10.1007/s00778-011-0241-y
Publisher
Springer Verlag
Citation
GUO, Xi; ZHENG, Baihua; ISHIKAWA, Yoshiharu; and GAO, Yunjun.
Direction-Based Surrounder Queries for Mobile Recommendations. (2011). VLDB Journal. 20, (5), 743-766.
Available at: https://ink.library.smu.edu.sg/sis_research/1409
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00778-011-0241-y
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons