• Journal of KIISE:Databases
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• v.27 no.2
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• pp.199-208
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• 2000

The k-nearest neighbor search query based on similarity is very important for content-based multimedia information retrieval(MIR). The conventional exact k-nearest neighbor search algorithm is not efficient for the MIR application because multimedia data should be represented as high dimensional feature vectors. Thus, an approximate k-nearest neighbor search algorithm is required for the MIR applications because the performance increase may outweigh the drawback of receiving approximate results. For this, we propose a new approximate k-nearest neighbor search algorithm for high dimensional data. In addition, the comparison of the conventional algorithm with our approximate k-nearest neighbor search algorithm is performed in terms of retrieval performance. Results show that our algorithm is more efficient than the conventional ones.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.256-258
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• 1998

대용량의 멀티미디어 자료를 기반으로 하는 내용-기반 멀티미디어 검색 시스템에서 k-최근접 탐색 질의는 사용자의 매우 중요한 검색 질의 중에 하나이다. 하지만, 방대한 양의 멀티미디어 데이터베이스를 기반으로하는 경우에는 적중 에러 없는 정확(exact) k-최근접 데이터 탐색을 위해서 상당히 많은 디스크 접근 횟수가 요구된다. 본 논문에서는 X-트리에서의 정확 k-최근접 탐색 질의를 개선하고, 또한 사용자의 빠른 검색 성능을 위해 다소의 적중 에러는 허용한다 하더라도 디스크 접근 횟수를 줄이는 근사(approximate) k-최근접 탐색 알고리즘을 제안한다.

• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.151-160
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• 2012

Most of the distributed high-dimensional indexing structures provide a reasonable search performance especially when the dataset is uniformly distributed. However, in case when the dataset is clustered or skewed, the search performances gradually degrade as compared with the uniformly distributed dataset. We propose a method of improving the k-nearest neighbor search performance for the distributed vector approximation-tree based on the strongly clustered or skewed dataset. The basic idea is to compute volumes of the leaf nodes on the top-tree of a distributed vector approximation-tree and to assign different number of bits to them in order to assure an identification performance of vector approximation. In other words, it can be done by assigning more bits to the high-density clusters. We conducted experiments to compare the search performance with the distributed hybrid spill-tree and distributed vector approximation-tree by using the synthetic and real data sets. The experimental results show that our proposed scheme provides consistent results with significant performance improvements of the distributed vector approximation-tree for strongly clustered or skewed datasets.

• The Journal of the Korea Contents Association
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• v.17 no.8
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• pp.619-630
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• 2017

With the growing popularity of smart devices, various location based services have been providing to users. Recently, some location based social applications that combine social services and location based services have been emerged. The demands of a k-nearest neighbors(k-NN) query which finds k closest locations from a user location are increased in the location based social network services. In this paper, we propose an approximate k-NN query processing method for fast response time in a large number of users environments. The proposed method performs efficient stream processing using big data distributed processing technologies. In this paper, we also propose a modified grid index method for indexing a large amount of location data. The proposed query processing method first retrieves the related cells by considering a user movement. By doing so, it can make an approximate k results set. In order to show the superiority of the proposed method, we conduct various performance evaluations with the existing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.336-339
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• 2009

본 논문에서는 최단 경로 탐색 및 거리 계산의 필요성을 가지고 근사 인덱싱 방법의 후처리 부분을 제안한다. 근사 인덱싱 방법이란 오프라인에서 네트워크 공간상의 객체들을 유클리드 공간 상의 절대 좌표로 사상하여 인덱싱한 후, k-최근접 이웃 질의를 처리하는 방법이다. 그러나 기존 연구는 질의 점으로부터 각 정적 객체까지의 경로를 탐색해주지 않을 뿐만 아니라 착오 기각이 발생한다. 따라서 본 논문에서는 질의 점으로부터 k개의 정적 객체까지의 경로를 효과적으로 탐색할 수 있는 방법을 제안한다. 또한, 이 방법을 통하여 착오 기각 역시 완화시킬 수 있는 방법을 제안한다. 실험을 통하여 제안하는 방법이 기존 경로 탐색 기법들에 비해 노드 탐색 횟수 및 실행 성능이 크게 향상시킨 것으로 나타났다.