Abstract
With the development of wireless communications and positioning technologies, locations of moving objects are highly demanding services. The assumption of static data is majorly applied on previous researches on reverse furthest neighbor queries. However, the data are dynamic property in the real world. Even, the data-aware are uncertain due to the limitation of measuring equipment or the delay of data communication. To effectively find the influence of querying a large number of moving objects existing in boundary area vs querying results of global query area, we put forward dynamic reverse furthest neighbor query algorithms and probabilistic reverse furthest neighbor query algorithms. These algorithms can solve the query of weak influence set for moving objects. Furthermore, we investigate the uncertain moving objects model and define a probabilistic reverse furthest neighbor query, and then present a half-plane pruning for individual moving objects and spatial pruning method for uncertain moving objects. The experimental results show that the algorithm is effective, efficient and scalable in different distribution and volume of data sets.
This work is supported in part by National Natural Science Foundation of China (41301407), Natural Science Foundation of Jiangsu Province (BK20130819), CSC (201406835051), Innovation Funding (NJ20160028), Innovative and Entrepreneurial PHD(2015).
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Li, B. et al. (2016). Dynamic Reverse Furthest Neighbor Querying Algorithm of Moving Objects. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_18
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