• Journal of KIISE:Databases
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• v.37 no.4
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• pp.203-208
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• 2010

This paper deals with an efficient index scheduling technique based on Hilbert curve and MBR for k-NN query in a single wireless broadcast channel environment. Previous works have two major problems. One is that they need a long time to process queries due to the back-tracking problem. The other is that they have to download too many spatial data since they can not reduce search space rapidly. Our proposed method broadcasts spatial data based on Hilbert curve order where a distributed index table is also broadcast with each spatial data. Each entry of index table represents the MBR which groups spatial data. By predicting the unknown location of spatial data, our proposed index scheme allows mobile clients to remove unnecessary data and to reduce search space rapidly. As a result, our method gives the decreased tuning time and access latency.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.12
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• pp.1184-1187
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• 2011

In this letter, a planar monopole UWB antenna with dual band-notched characteristics is proposed. The band-stop characteristics is realized by embedding the 1st/3rd order Hilbert-curve slots on the patch. With the dimension adjustment of each Hilbert-curve slots, the band rejection from 3.3 to 3.7 GHz and from 5.3 to 6 GHz can be accomplished easily. The VSWR and radiation pattern of the fabricated antenna are measured, and the proposed antenna would be adequate to a UWB applications.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.23-30
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• 2004

In order to approximate the spatial query result size we partition the input rectangles into subsets and estimate the query result size based on the partitioned spatial area. In this paper we examine query result size estimation in skewed data. We examine the existing spatial partitioning techniques such as equi-area and equi-count partitioning, which are analogous to the equi-width and equi-height histograms used in relational databases, and examine the other partitioning techniques based on spatial indexing. In this paper we propose a new spatial partitioning technique based on the Hilbert space filling curve. We present a detailed experimental evaluation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. The experiments showed that the proposed partitioning technique based on the Hilbert space filling curve achieves better query result size estimation than the existing techniques for space query size, bucket numbers, skewed data, and spatial data size.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.46-48
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• 2003

최근 시공간 데이타에 대한 OLAP연산 효율을 증가시키기 위한 여러 가지 연구들이 행하여지고 있다. 이들 연구의 대부분은 다중트리구조에 기반하고 있다. 다중트리구조는 공간차원을 색인하기 위한 하나의 R-tree와 시간차원을 색인하기 위한 다수의 B-tree로 이루어져 있다. 하지만, 이러한 다중트리구조는 높은 유지비용과 불충분한 질의 처리 효율로 인해 현실적으로 시공간 OLAP연산에 적용하기에는 어려운 점이 있다. 본 논문에서는 이러한 문제를 근본적으로 개선하기 위한 접근 방법으로서 힐버트큐브(Hilbert Cube, H-Cube)를 제안하고 있다. H-Cube는 집계질의(aggregation query) 처리 효율을 높이기 위해 힐버트 곡선을 이용하여 셀들에게 완전순서(total-order)를 부여하고 있으며, 아울러 전통적인 누적합(prefix-sum) 기법을 함께 적용하고 있다. H-Cube는 적응적이며, 완전순서화되어 있으며, 또한 누적합을 이용한 셀 기반의 색인구조이다. 본 논문에서는 H-Cube의 성능 평가를 위해서 다양한 실험을 하였으며, 그 결과로서 유지비용과 질의 처리 효율성면 모두에서 다중트리구조보다 높은 성능 향상이 있음을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.55-57
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• 2002

공간 데이터베이스의 규모는 매우 방대하여 질의 처리에 많은 비용이 발생한다. 따라서 효율적인 질의 처리를 위해서는 질의 수행 결과의 예측이 필요하다. 이를 위해 실제 공간 데이터의 특성을 근접하게 나타내는 요약 데이터를 생성하여 그 결과를 통해 질의 결과의 크기를 추정하게 된다. 기존의 공간 데이터 요약 기법으로는 면적 균등 분할 기법, 개수 균등 분할 기법, 인덱스 분활 기법 등이 있다. 본 논문에서는 기존에 연구된 다양한 분말 기법에 대해 알아보고, 힐버트 공간 재움 곡선 방법에 개수 균등 분말 기법을 적용시킨 새로운 공간 분할 방법을 제안하여 기존의 방법과 새로운 방법의 성능을 비교한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.49-53
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• 2002

본 논문은 광학 현미경으로 관찰된 데이터들을 분산 시스템이나 병렬 시스템에 구현한 소프트웨어 시스템의 설계에 대한 것으로, 이 시스템이 처리하는 데이터들이 대용량이라는 특성과 함께 다중 해상도의 특성을 갖는다. 본 시스템은 고객/서버 모델을 기반으로 하였으며, 대용량 데이터 처리시 성능에 중요한 디스크 입출력의 대역폭을 높이기 위해 힐버트 곡선 기반의 분산 알고리즘을 적용하였다. 서버부는 조정자 노드와 서비스 노드로 구성되며, 시스템의 제 구성 요소들간에는 정해진 통신 규약에 따라 메시지를 주고 받고, 상호 독립적이다. 이 시스템은 의학 교육, 원격 병리, 가상 학술 회의 등에 응용될 때 활용 가치가 높을 것으로 기대된다.

• Journal of KIISE:Databases
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• v.30 no.5
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• pp.451-463
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• 2003

Recently, there have been various research efforts to develop strategies for accelerating OLAP operations on huge amounts of spatio-temporal data. Most of the work is based on multi-tree structures which consist of a single R-tree variant for spatial dimension and numerous B-trees for temporal dimension. The multi~tree based frameworks, however, are hardly applicable to spatio-temporal OLAP in practice, due mainly to high management cost and low query efficiency. To overcome the limitations of such multi-tree based frameworks, we propose a new approach called Hilbert Cube(H-Cube), which employs fractals in order to impose a total-order on cells. In addition, the H-Cube takes advantage of the traditional Prefix-sum approach to improve Query efficiency significantly. The H-Cube partitions an embedding space into a set of cells which are clustered on disk by Hilbert ordering, and then composes a cube by arranging the grid cells in a chronological order. The H-Cube refines cells adaptively to handle regional data skew, which may change its locations over time. The H-Cube is an adaptive, total-ordered and prefix-summed cube for spatio-temporal data warehouses. Our approach focuses on indexing dynamic point objects in static spatial dimensions. Through the extensive performance studies, we observed that The H-Cube consumed at most 20% of the space required by multi-tree based frameworks, and achieved higher query performance compared with multi-tree structures.

• Journal of KIISE:Software and Applications
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• v.26 no.12
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• pp.1404-1417
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• 1999

웹과 같은 분산 환경에서는, 웹 브라우저 상에서 SQL 형식의 공간 질의를 수행시키는 것과 또한 서버로부터 그 질의 결과를 보는 것이 가능하다. 그러나, 격자 이미지(raster image)와 같은 대용량 공간 데이타를 포함하는 질의 결과를 웹 브라우징할 때 발생하는 많은 문제점들 중에서, 사용자 응답 시간의 지연은 매우 중요한 문제이다. 본 논문에서는 사용자의 재요청(callback) 접근 패턴이 공간적 근접성(spatial locality)을 따른다는 가정하에서의, 사용자 응답 시간을 최소화하기 위한 새로운 프리페치(prefetch) 전략에 대해서 서술한다. 본 논문의 프리페치 전략은 다음과 같이 요약될 수 있다. 첫째, 프리페치 알고리즘은 사용자의 접근 패턴을 잘 반영하는 힐버트 곡선(Hilbert-curve) 모델을 바탕으로 한다. 둘째, 프리페치 전송 비용을 줄이기 위해서 사용자의 재요청 시간 간격(think time)을 이용한다. 본 논문에서는, 힐버트 곡선을 이용한 프리페치 전략의 성능 평가를 위해서 다양한 실험을 하였으며, 그 결과로 프리페치를 하지 않는 방식보다 높은 성능 향상이 있음을 보인다.Abstract In distributed environment(e.g., WWW), it would be possible for the users to submit SQL-like spatial queries and to see their query results from the server on the Web browser. However, of many obstacles which result from browsing query results including large spatial data such as raster image, the delay of user response time is very critical. In this paper we present a new prefetch policy which can alleviate user response time on the assumption that user's callback access pattern has spatial locality. Our prefetch policy can be summerized as follows: 1) our prefetch algorithm is based on the Hibert-curve model which well replects user's access pattern, and 2) it utilizes user's callback interval to reduce prefetch network transmission cost. In this paper we conducted diverse experiments to show that our prefetch policy achieves higher performance improvement compared to other non-prefetch methods.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.11-23
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• 2007

Applications involving moving objects require index structures to handle frequent updates of objects' locations efficiently. Several methods to index the current, the past and the future positions of moving objects have been proposed for the applications. Most of them are based on R-tree like index structures. Some researches have made efforts to improve update performance of R-trees that are actually focused on query performance. Even though the update performance is improved by researchers' efforts, the overhead and immaturity of concurrency control algorithms of R-trees makes us hesitate to choose them for moving objects. In this paper, we propose an update efficient indexing method that can be applicable for indexing the past, the current and the future locations. The proposed index is based on B+-Trees and Hilbert curve. We present an advanced Hilbert curve that adjusts automatically the order of Hilbert curve in subregions according to the data distribution and the number of data objects. Through empirical studies, we show that our strategy achieves higher response time and throughput.

• Journal of Information Technology and Architecture
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• v.10 no.1
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• pp.79-91
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• 2013

This paper proposes a systematic methodology that could be used to decide which one shows the best performance among space filling curves (SFCs) in applying lower-dimensional transformations to histogram sequences. A histogram sequence represents a time-series converted from an image by the given SFC. Due to the high-dimensionality nature, histogram sequences are very difficult to be stored and searched in their original form. To solve this problem, we generally use lower-dimensional transformations, which produce lower bounds among high dimensional sequences, but the tightness of those lower-bounds is highly affected by the types of SFC. In this paper, we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality, which comes from an intuition of "if the entries are adjacent in a histogram sequence, their corresponding cells should also be adjacent in its original image." We also propose spatial locality preservation metric (slpm in short) that quantitatively evaluates spatial locality and present its formal computation method. We then evaluate five SFCs from the perspective of slpm and verify that this evaluation result concurs with the performance evaluation of lower-dimensional transformations in real image matching. Finally, we perform k-NN (k-nearest neighbors) search based on lower-dimensional transformations and validate accuracy of the proposed slpm by providing that the Hilbert-order with the highest slpm also shows the best performance in k-NN search.