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Top-k most influential location selection.
| Content Provider | CiteSeerX |
|---|---|
| Author | Huang, Jin Zhang, Rui Wen, Zeyi Chen, Jian Qi, Jianzhong He, Zhen |
| Abstract | We propose and study a new type of facility location selection query, the top-k most influential location selection query. Given a set M of customers and a set F of existing facilities, this query finds k locations from a set C of candidate locations with the largest influence values, where the influence of a candidate location c (c ∈ C) is defined as the number of customers in M who are the reverse nearest neighbors of c. We first present a naive algorithm to process the query. However, the algorithm is computationally expensive and not scalable to large datasets. This motivates us to explore more efficient solutions. We propose two branch and bound algorithms, the Estimation Expanding Pruning (EEP) algorithm and the Bounding Influence Pruning (BIP) algorithm. These algorithms exploit various geometric properties to prune the search space, and thus achieve much better performance than that of the |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Influential Location Selection Candidate Location Search Space Naive Algorithm New Type Bound Algorithm Influence Value Influential Location Selection Query Estimation Expanding Pruning Facility Location Selection Query Efficient Solution Large Datasets Various Geometric Property Bounding Influence Pruning |
| Content Type | Text |
