Abstract
This paper presents a nearest user-specified group (NUG) search which called a clustered NN problem. Given a set of data points P and a query point q, NUG search finds the nearest subset c ⊂ P (|c| ≥ k) from q (called user-specified group) that satisfies given conditions. Motivated by the brute-force approach for NUG search requires O(|P|2) computational cost, we propose a faster algorithm to handle NUG problem with in-memory processing. We first define clustered objects above k as a user-specified group and the NUG search problem. Moreover, the proposed solution converts a NUG search problem to a graph formulation problem, and reduces processing cost with geometric-based heuristics. Our experimental results show that the efficiency and effectiveness of our proposed approach outperforms the conventional one.
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Spatial Data Generator by Yannis Theodoridis
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© 2015 Springer Science+Business Media Singapore
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Jang, HJ., Choi, WS., Hyun, KS., Lim, T., Jung, SY., Chung, J. (2015). In-Memory Processing for Nearest User-Specified Group Search. In: Park, DS., Chao, HC., Jeong, YS., Park, J. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-10-0281-6_112
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DOI: https://doi.org/10.1007/978-981-10-0281-6_112
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Publisher Name: Springer, Singapore
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