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2017 IEEE 33rd International Conference on Data Engineering (2017)
San Diego, California, USA
April 19, 2017 to April 22, 2017
ISSN: 2375-026X
ISBN: 978-1-5090-6543-1
pp: 21-22
The location selection (LS) problem aims to mine the optimal location to place a new facility from a set of candidates such that the benefit or influence on a given set of objects is maximized. State-of-the-art LS techniques assume each object is static and can only be influenced by a single facility. However, in reality, objects (e.g., people, vehicles) are mobile and are influenced by multiple facilities. Consequently, classical LS solutions fail to select locations accurately. In this work, we introduce a generalized LS problem called PRIME-LS which takes mobility and probability factors into consideration to address the aforementioned limitations. To solve the problem, we propose an algorithm called PINOCCHIO, which leverages two pruning rules based on a novel distance measure, and further extend it by incorporating two optimization strategies. Experimental study over two real-world datasets demonstrates superiority of our framework in comparison to state-of-the-art LS techniques.
Probabilistic logic, Computer science, Mobile communication, Optimization, Conferences, Data engineering, Information technology

M. Wang, H. Li, J. Cui, K. Deng, S. S. Bhowmick and Z. Dong, "PINOCCHIO: Probabilistic Influence-Based Location Selection over Moving Objects," 2017 IEEE 33rd International Conference on Data Engineering(ICDE), San Diego, California, USA, 2017, pp. 21-22.
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