• Journal of KIISE:Databases
• /
• v.29 no.1
• /
• pp.58-71
• /
• 2002

Generally, multi-dimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amount of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel high-dimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-n$\times$mD(disk) architecture which is the hybrid type of nP-nD and lP-nD. Its node structure increases fan-out and reduces the height of a index tree. Also, A range search algorithm that maximizes I/O parallelism is devised, and it is applied to K-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• Journal of Computing Science and Engineering
• /
• v.9 no.4
• /
• pp.204-213
• /
• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.

• Journal of KIISE:Databases
• /
• v.36 no.2
• /
• pp.73-83
• /
• 2009

In this paper we propose an object similarity matching method based on shape characteristics of an object in an image. The proposed method extracts edge points from edges of objects and generates a log polar histogram with respect to each edge point to represent the relative placement of extracted points. It performs the matching in such a way that it compares polar histograms of two edge points sequentially along with edges of objects, and uses a well-known k-NN(nearest neighbor) approach to retrieve similar objects from a database. To verify the proposed method, we've compared it to an existing Shape-Context method. Experimental results reveal that our method is more accurate in object matching than the existing method, showing that when k=5, the precision of our method is 0.75-0.90 while that of the existing one is 0.37, and when k=10, the precision of our method is 0.61-0.80 while that of the existing one is 0.31. In the experiment of rotational transformation, our method is also more robust compared to the existing one, showing that the precision of our method is 0.69 while that of the existing one is 0.30.

• Proceedings of the Korean Information Science Society Conference
• /
• 2011.06c
• /
• pp.45-48
• /
• 2011

본 논문에서는 다양한 타입의 위치기반 데이터들을 하나의 R-tree로 통합합 $R^m$-tree의 구조와 이 $R^m$-tree를 이용하여 질의 포인트로부터 각 타입에서 k개의 가까운 위치기반 데이터를 찾는 mkNN(multi-type k nearest neighbor) 질의 처리기법을 제안하였다. 특히, 다양한 타입의 위치기반 데이터들을 각 타입별로 독립된 R-tree로 유지하지 않고, 하나의 $R^m$-tree로 통합하여 관리함으로써 mkNN 질의 처리시 같은 레벨의 공간의 반복탐색을 줄일 수 있도록 고안하였다. 그리고 각 타입 t에 대한 위치데이터를 관리하는 부가적인 타입정보 자료구조로서 위치정보를 담은 TMBR, 데이터 개수정보를 담은 $I_t$-entry를 새로이 고안하여 mkNN질의 처리시 효율적인 휠터링(filtering)과 검색과정이 이루어지도록 하였다.

• Journal of KIISE:Databases
• /
• v.28 no.1
• /
• pp.86-94
• /
• 2001

구형 피라미드 기법[1,2]은 d-차원의 공간을 2d개의 구형 피라미드들로 분할하는 특별한 공간 분할 방식을 이용하여 고차원 데이터를 효율적으로 색인할 수 있는 새로운 색인 방법으로 제안되었다. 구형 피라미드 기법은 구형태의 영역질의를 처리하는 알고리즘을 제안하였으나 유사 검색에 많이 사용되는 또 다른 종류의 질의인 최근접 질의를 처리하는 알고리즘을 제안하지 못했다. 본 논문에서는 점진적 최근접 질의 처리 알고리즘을 확장하여 구형피라미드 기법 상에서 효율적으로 최근접 질의를 처리하는 알고리즘을 제안한다. 마지막으로, R*-tree와 X-tree 상에서 구현된 점진적 k-최근접 질의 처리 방법과 다양한 비교 실험을 통하여 구형 피라미드 기법을 이용한 k-최근접 질의 처리 방법이 더 효율적임을 보인다.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.869-872
• /
• 2000

Although many content-based image retrieval systems using shape feature have tried to cover rotation-, position- and scale-invariance between images, there have been problems to cover three kinds of variance at the same time. In this paper, we introduce new approach to extract shape feature from image using MBR(Minimum Bounding Rectangle). The proposed method scans image for extracting MBR information and, based on MBR information, compute contour information that consists of 16 points. The extracted information is converted to specific values by normalization and rotation. The proposed method can cover three kinds of invariance at the same time. We implemented our method and carried out experiments. We constructed R*_tree indexing structure, perform k-nearest neighbor search from query image, and demonstrate the capability and usefulness of our method.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.96-112
• /
• 2020

Solving the disclosure problem of sensitive information with the k-nearest neighbor query, location dummy technique, or interfering data in location-based services (LBSs) is a new research topic. Although they reduced security threats, previous studies will be ineffective in the case of sparse users or K-successive privacy, and additional calculations will deteriorate the performance of LBS application systems. Therefore, a model is proposed herein, which is based on geohash-encoding technology instead of latitude and longitude, memcached server cluster, encryption and decryption, and authentication. Simulation results based on PHP and MySQL show that the model offers approximately 10× speedup over the conventional approach. Two problems are solved using the model: sensitive information in LBS application is not disclosed, and the relationship between an individual and a track is not leaked.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.10a
• /
• pp.190-192
• /
• 2001

시간에 따라 공간적 위치가 연속적으로 변하는 객체를 이동객체라 한다. 이동객체에 대한 최근접 질의는 시간에 따라 거리도 달라지게 됨으로써 기존의 방법을 그대로 이용하기 어렵다. 본 논문에서는 이동객체에 대한 최근접 질의를 3D R-tree를 이용한 최근접 탐색방법에 시간적 요소를 추가하여 처리하였다. 또, 3D R-tree를 구성하는 노드의 크기에 따른 사장공간(Dead Space)이 최근접 질의에 미치는 영향을 분석하였다. 실험결과에 따르면 이동객체를 하나의 노드에 저장하면 사장공간이 증가하여 불필요한 노드를 접근함으로써 성능이 떨어지고 여러 개로 분할하여 저장하게 되면 색인의 크기가 커짐으로써 질의 처리 성능이 떨어지게 된다. 따라서, 이동객체에 대한 적절한 분할 및 병합 정책이 필요하다는 것을 알 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.1410-1411
• /
• 2012

최근 모바일 사용자의 안전한 위치기반 서비스의 사용을 위한 아웃소싱 데이터베이스에서 객체 및 사용자의 위치 정보를 보호하는 연구가 위치 데이터를 보호하기 위한 연구가 활발히 진행되고 있다. 그러나 기존 연구는 불필요한 객체 정보를 요구하기 때문에, 높은 질의 처리 시간을 지니는 단점을 지닌다. 이러한 문제점을 해결하기 위해, 본 논문에서는 기준 POI를 중심으로 객체의 방향성 정보와 변환된 거리를 이용하여, 사용자와 객체의 정보를 보호하는 k-최근접 질의처리 알고리즘을 제안한다.

• International Journal of Internet, Broadcasting and Communication
• /
• v.15 no.2
• /
• pp.22-30
• /
• 2023

The portable mobile devices with high performance and high speed 5G network activate and explode the demands for ubiquitous information services that remove the limitations of time for the communication and places to request for the information. NN (Nearest Neighbor) search is one of the most important types of queries to be processed efficiently in the information services. Various indexes have been proposed to support efficient NN search in the wireless broadcast system. The indexes adopting Hilbert curve, grid partition or Voronoi diagram enable the clients to search for NN quickly in the wireless broadcast channel. It is necessary that an efficient means to evaluate the performances of various indexes. In this paper, we propose an open framework that can adopt a variety of indexing schemes and evaluate and compare the performances of them. The proposed framework is organized with open and flexible structure that can adopt hybrid indexing schemes extensible to Voronoi diagram as well as simple indexing schemes. With the implemented framework, we demonstrate the efficiency and scalability and flexibility of the proposed framework by evaluating various indexing schemes for NN query.