Analysis and evaluation of the top- $$k$$ k most influential location selection query

Jian Chen, Jin Huang, Zeyi Wen, Zhen He, Kerry Taylor, Rui Zhang
2014 Knowledge and Information Systems  
In this paper, we propose a new type of queries to retrieve the topk most influential locations from a candidate set C given sets of customers M and existing facilities F . The influence models the popularity of a facility. Such queries have wide applications in decision support systems. A naive solution sequentially scans (SS) all data sets, which is expensive and hence we investigate two branch-and-bound algorithms for the query, namely Estimate Expanding Pruning (EEP) and Bounding Influence
more » ... runing (BIP). Both algorithms follow the best first traverse. On determining the traversal order, while EEP leverages distance metrics between nodes, BIP relies on half plane pruning which avoids the repetitive estimations in EEP. As our experiments shown,
doi:10.1007/s10115-013-0720-0 fatcat:h7mtqrwfjvbtfl22ggwxpnwxvm