TY - GEN
T1 - The flexible group spatial keyword query
AU - Ahmad, Sabbir
AU - Kamal, Rafi
AU - Ali, Mohammed Eunus
AU - Qi, Jianzhong
AU - Scheuermann, Peter
AU - Tanin, Egemen
N1 - Funding Information:
Acknowledgment. This research is partially supported by the ICT Division, Government of the People’s Republic of Bangladesh. Jianzhong Qi is supported by The University of Melbourne Early Career Researcher Grant (project number 603049).
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - We propose the flexible group spatial keyword query and algorithms to process three variants of the query in the spatial textual domain: (i) the group nearest neighbor with keywords query, which finds the data object that optimizes the aggregate cost function for the whole group Q of size n query objects, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup of query objects and the data object 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 data objects 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 conduct extensive experiments with two real datasets to show the efficiency of the proposed algorithms.
AB - We propose the flexible group spatial keyword query and algorithms to process three variants of the query in the spatial textual domain: (i) the group nearest neighbor with keywords query, which finds the data object that optimizes the aggregate cost function for the whole group Q of size n query objects, (ii) the subgroup nearest neighbor with keywords query, which finds the optimal subgroup of query objects and the data object 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 data objects 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 conduct extensive experiments with two real datasets to show the efficiency of the proposed algorithms.
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U2 - 10.1007/978-3-319-68155-9_1
DO - 10.1007/978-3-319-68155-9_1
M3 - Conference contribution
AN - SCOPUS:85030673831
SN - 9783319681542
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 16
BT - Databases Theory and Applications - 28th Australasian Database Conference, ADC 2017, Proceedings
A2 - Xiao, Xiaokui
A2 - Cao, Xin
A2 - Huang, Zi
PB - Springer Verlag
T2 - 28th Australasian Database Conference, ADC 2017
Y2 - 25 September 2017 through 28 September 2017
ER -