In-memory processing for nearest user-specified group search

Hong Jun Jang; Woo Sung Choi; Kyeong Seok Hyun; Taehyung Lim; Soon Young Jung; Jaehwa Chung

doi:10.1007/978-981-10-0281-6_112

In-memory processing for nearest user-specified group search

Hong Jun Jang, Woo Sung Choi, Kyeong Seok Hyun, Taehyung Lim, Soon Young Jung, Jaehwa Chung

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Lecture Notes in Electrical Engineering
Publisher	Springer Verlag
Pages	797-803
Number of pages	7
DOIs	https://doi.org/10.1007/978-981-10-0281-6_112
Publication status	Published - 2015

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	373
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media Singapore 2015.

Keywords

K-nearest neighbor query
Nearest user-specified group query

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-981-10-0281-6_112

Cite this

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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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AU - Jang, Hong Jun

AU - Choi, Woo Sung

AU - Hyun, Kyeong Seok

AU - Lim, Taehyung

AU - Jung, Soon Young

AU - Chung, Jaehwa

PY - 2015

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In-memory processing for nearest user-specified group search

Abstract

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Keywords

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