• Journal of Digital Convergence
• /
• v.3 no.1
• /
• pp.149-163
• /
• 2005

Researches for NN(nearest neighbor) query which is often used in LBS system, have been worked. However. Conventional NN query processing techniques are usually meaningless in moving object management system for LBS since their results may be invalidated as soon as the query and data objects move. To solve these problems, in this paper we propose a new nearest neighbor query processing technique, called CTNN, which is possible to meet continuous trajectory nearest neighbor query processing. The proposed technique consists of Approximate CTNN technique which has quick response time, and Exact CTNN technique which makes it possible to search accurately nearest neighbor objects. Experimental results using GSTD datasets shows that the Exact CTNN technique has high accuracy, but has a little low performance for response time. They also shows that the Approximate CTNN technique has low accuracy comparing with the Exact CTNN, but has high response time.

• Proceedings of the Korea Contents Association Conference
• /
• 2017.05a
• /
• pp.43-44
• /
• 2017

다양한 디지털 기기 활용의 증가로 인해 멀티미디어 데이터가 증가됨에 따라 내용 기반으로 검색하는 기술이 연구되고 있다. 내용 기반 검색을 위해 멀티미디어에서 추출된 고차원 특징 벡터가 대용량이 되면서 고차원 데이터를 분산해서 관리하는 색인 기법이 필요하다. 본 논문에서는 대용량 멀티미디어 데이터에서 유사한 이미지를 검출하기 위한 분산 고차원 색인 기법을 제안한다. 제안하는 기법은 마스터/슬레이브 구조로 되어 있다. 마스터 서버의 색인 구조는 그리드 방식을 사용하여 검색 요청 시 탐색하는 노드를 감소시킨다. 슬레이브 서버의 색인 구조는 구 형태로 색인하여 범위 질의와 최근접 질의를 효율적으로 검색한다.

• Journal of KIISE:Software and Applications
• /
• v.29 no.3
• /
• pp.192-201
• /
• 2002

Automated text classification is considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been addressed with machine learning technologies such as k-nearest neighbor, decision tree, support vector machine and neural networks. However, only few investigations in text classification are studied on real problems but on well-organized text corpus, and do not show their usefulness. This paper proposes and analyzes text classification methods for a real application, email document classification task. First, we propose a combining method of multiple neural networks that improves the performance through the combinations with maximum and neural networks. Second, we present another strategy of combining multiple machine learning classifiers. Voting, Borda count and neural networks improve the overall classification performance. Experimental results show the usefulness of the proposed methods for a real application domain, yielding more than 90% precision rates.

• The Korean Journal of Applied Statistics
• /
• v.17 no.3
• /
• pp.605-617
• /
• 2004

One of the best important thing in data mining process is the quality of data used. When we perform the mining on data with excellent quality, the potential value of data mining can be improved. In this paper, we propose the data fusion technique for data enrichment that one phase can improve data quality in KDD process. We attempted to add k-NN technique to the regression technique, to improve performance of fusion technique through reduction of the loss of information. Simulations were performed to compare the proposed data fusion technique with the regression technique. As a result, the newly proposed data fusion technique is characterized with low MSE in continuous fusion variables.

• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.3
• /
• pp.13-21
• /
• 2011

In mobile environment supporting mobility technologies, user requirements have been increased with respect to utilization of location information. In particular, moving object DBMS has consistently posed in order to efficiently maintain traffic information related to location of vehicle which tents to tremendously change over time. Despite the fact that these sorts of researches must be taken into consideration, empirical studies on moving object in terms of map database for lbs service, spatial attribute of which is continuously changed over time, have rarely performed. Therefore, aim of this paper is to suggest efficient spatial index scheme, which is capable of supporting query processing algorithm and location of moving object over time, by developing new empirical model. As a result, we can come to the conclusion that moving object model based on multi-fixed grid index makes it possible to cut down on the number of entity for retrieving. What's more, this model enables hierarchical data to be accessed through efficient spatial filtering on large-scale lbs data and constraints in accordance with level in order to display map.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.7
• /
• pp.1518-1524
• /
• 2014

In this paper, a new method of spatial query, which is called Surround Search (SuSe) is suggested. This method makes it possible to search for the closest spatial object of interest to the user from a query point. SuSe is differentiated from the existing spatial object query schemes, because it locates the closest spatial object of interest around the query point. While SuSe searches the surroundings, the spatial object is saved on an R-tree, and MINDIST, the distance between the query location and objects, is measured by considering an angle that the existing spatial object query methods have not previously considered. The angle between targeted-search objects is found from a query point that is hidden behind another object in order to distinguish hidden objects from them. The distinct feature of this proposed scheme is that it can search the faraway or hidden objects, in contrast to the existing method. SuSe is able to search for spatial objects more precisely, and users can be confident that this scheme will have superior performance to its predecessor.

• Journal of Internet Computing and Services
• /
• v.10 no.6
• /
• pp.1-6
• /
• 2009

In this paper, we propose an energy efficient index scheme for Nearest Neighbor queries, specially designed for mobile P2P environments. we propose a PMBR (P2P Minimum Boundary Rectangle) index that provides the client with the ability of selective contacting and tuning from other nodes. The mobile client is able to identify whether or not the neighbor nodes have the desired information by accessing PMBR. Thus, the client immediately switches to change to another node's broadcast channel if the index does not contain the desired information. Furthermore, the client is able to predict the arrival time of the desired data items and only needs to tune into the broadcast channel when the requested data items arrives, by first accessing PMBR. Thus, the mobile client can stay in power save mode most of time, and tune into the broadcast channel only when the requested data items arrive. Experiments are conducted to evaluate the performance of the proposed scheme. Comprehensive experiments illustrate that the proposed scheme is more efficient than the previous techniques in terms of energy consumption.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.10
• /
• pp.79-89
• /
• 2019

While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.

• Journal of KIISE:Computing Practices and Letters
• /
• v.15 no.12
• /
• pp.948-952
• /
• 2009

Recently, a new distributed grid scheme, called DS-GRID(distributed S-GRID), has been proposed to manage the location information of moving objects in a spatial network[1]. However, because DS-GRID uses uniform grid cells, it cannot handle skewed data which frequently occur in the real application. To solve this problem, we propose a dynamic distributed grid scheme which splits a grid cell dynamically based on the density of moving objects. In addition, we propose a k-nearest neighbor processing algorithm for the proposed scheme. Finally, it is shown from the performance analysis that our scheme achieves better retrieval and update performance than the DS-GRID when the moving objects are skewed.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.5
• /
• pp.189-196
• /
• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.