• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.21-24
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• 2011

최근 NAND 플래시 메모리는 가벼운 무게, 적은 전력소모, 온도 및 충격에 강한 내구성 때문에 하드디스크를 대체할 저장 매체로 주목 받고 있다. 하지만 NAND 플래시 메모리는 비대칭적인 읽기 쓰기 소거 연산 처리 속도와 제자리 갱신이 불가능한 물리적인 특징으로 인해 디스크 기반의 대표적인 인덱스 구조 중의 하나인 해시 인덱스 구조를 NAND 플래시 메모리 상에 구현하였을 때, 레코드가 빈번하게 삽입, 삭제, 갱신되면 대량의 제자리 갱신이 발생하여 플래시 메모리에서 느린 쓰기 연산과 소거 연산이 수행되어 성능이 저하된다. 본 논문에서는 이러한 성능 저하를 피하기 위하여 버켓 오버플로우 발생 시 분할 연산을 수행하지 않고, 최대한 지연시킴으로써 쓰기 연산을 줄이는 인덱스 구조를 제안한다. 또한, 각 버켓에 대한 오버플로우 버켓의 갱신 및 삭제 비율에 따라 적응적으로 오버플로우 버켓을 할당하여 추가적인 읽기 쓰기 연산을 줄인다. 본 논문은 기존의 해시 인덱스 구조를 예제 및 수식을 통하여 제안하는 인덱스 구조의 우수성을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1493-1496
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• 2003

데이터베이스 관계 연산자 중 프로젝션(projection)과 집단 연산(aggregate function)시 사용되는 GROUP BY절, 그리고 동등 조인(equi join)에 대한 질의 처리는 중복된 튜플 중복된 GROUP BY 필드, 조인 중 발생하는 임시결과에 대한 제거나 집단 연산, 임시 결과의 저장을 위해 정렬이나 해싱 기반 알고리즘을 적용하고 있다. 이 중 해싱 기반 알고리즘은 데이터에 대한 직접적인 접근 방법과 정렬비용이 없다는 장점으로 인해 자주 사용하게 된다. 그러나 이러한 해싱(extendible hashing)[1] 기반 알고리즘은 키 값이 저장되는 버켓(bucket) 페이지의 넘침(overflow)으로 인해 분할(split)이 발생하는 경우, 분할을 야기시킨 버켓 페이지에 대한 정보를 제외한 동일한 내용의 기존 디렉토리 구조를 배로 확장해야 하는 공간 확장과, 확장된 디렉토리 구조의 유지를 위해 많은 비용을 소모하게 된다. 본 논문에서는 다량의 데이터에 대한 접근 기법과 디렉토리 구조의 저장공간, 유지 비용 절감 및 중복 해시 값을 지니는 데이터를 처리하기위한 해시 색인인 가상 디렉토리 확장 해시 색인을 제안한다. 가상 디렉토리 확장 해시 색인은 디렉토리 구조를 다단계 구조로 유지함으로써, 넓은 저장 공간을 필요로 하는 다량의 데이터에 대한 접근경로 문제를 해결하였고, 가상 디렉토리 레벨이라는 새로운 구조를 통해, 기존 디렉토리 구조의 공간 낭비 및 유지 비용을 최소화 시켰으며, 버켓 페이지를 리스트(list) 구조로 유지함으로써 중복 해시 값에 의한 디렉토리 구조의 연쇄적 분할 문제를 해결하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.23-32
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• 2004

The query optimizer's important task while a query is invoked is to estimate the fraction of records in the databases that satisfy the given query condition. The query result size estimation in spatial databases, like relational databases, proceeds to partition the whole input into a small number of subsets called “buckets” and then estimate the fraction of the input in the buckets. The accuracy of estimation is determined by the difference between the real data counts and approximations in the buckets, and is dependent on how to partition the buckets. Existing techniques for spatial databases are equi-area and equi-count techniques, which are respectively analogous in relation databases to equi-height histogram that divides the input value range into buckets of equal size and equi-depth histogram that is equal to the number of records within each bucket. In this paper we propose a new partitioning technique that determines buckets according to the maximal difference of area which is defined as the product of data ranges End frequencies of input. In this new technique we consider both data values and frequencies of input data simultaneously, and thus achieve substantial improvements in accuracy over existing approaches. We present a detailed experimental study of the accuracy of query result size estimation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. Experiments confirm that our proposed techniques offer better accuracy in query result size estimation than the existing techniques for space query size, bucket number, data number and data size.

• The Transactions of the Korea Information Processing Society
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• v.3 no.1
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• pp.63-76
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• 1996

In this paper, we propose a new dynamic spatial index structure, called PR -file, for handling spatial objects and the modified hierarchical variance which measures the degree of spatial locality at each level. Under the assumption that a multidimensional search space has a circular domain, PR-file uses the modified hierarchical variance for clustering spatially adjacent objects. The insertion and splitting algorithms of PR_file preserve and index which has a low hierarchical variance regardless of object distributions. The simulation result shows that PR- file has a high hit ratio during a retrieval of objects by using an index with low hierarchical variance. And it shows a characteristic that the larger the bucket capacity, the higher the bucket utilization.

• Spatial Information Research
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• v.19 no.3
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• pp.95-106
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• 2011

Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part, in-network aggregate query processing techniques are studied intensively in wireless sensor networks. Existing representative in-network aggregate query processing techniques propose routing algorithms and data structures for processing aggregate queries. However, these aggregate query processing techniques have problems such as high energy consumption in sensor nodes, low accuracy of query processing results, and long query processing time. In order to solve these problems and to enhance the efficiency of aggregate query processing in wireless sensor networks, this paper proposes Bucket-based Parallel Aggregation(BPA). BPA divides a query region into several cells according to the distribution of sensor nodes and builds a Quad-tree, and then processes aggregate queries in parallel for each cell region according to routing. And it sends data in duplicate by removing redundant data, which, in turn, enhances the accuracy of query processing results. Also, BPA uses a bucket-based data structure in aggregate query processing, and divides and conquers the bucket data structure adaptively according to the number of data in the bucket. In addition, BPA compresses data in order to reduce the size of data in the bucket and performs data transmission filtering when each sensor node sends data. Finally, in this paper, we prove its superiority through various experiments using sensor data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1843-1850
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• 2004

If the location information of a great number of cars kept for business with telematics terminals is acquired and managed efficiently, this information forms the foundation for controlling cars and traffic flows. The studies on the pure spatial indices have focused on the efficient retrievals. However, the acquisition and management of the terminal location of moving objects are more important than the efficiency of the query processing in the moving object databases. Therefore, it will be need to adopt parallel processing system for the moving object databases which should maintain the object's current location as precise as possible. This paper proposes a location management system using CDMA communications of telematics terminals. More precisely, we propose a architecture of spatial indexing mobile objects using multiple processors, and also newly propose a method of splitting buckets using the properties of moving objects in order to minimize the number of database updates. We also propose a acquisition method for gathering the location information of moving objects and passing the information of the bucket extents in order to reduce the amount of passed messages between processors.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.927-934
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• 2003

Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.345-349
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• 2004

In order to index exactly moving objects(vehicle, mobile phone, PDA, etc.) in the mobile database, continuous updates of their locations are inevitable as well as time-consuming. The studies of pure spatial indices have focused on the efficient retrievals. However, the acquisition and management of the terminal Location of moving objects are more important than the efficiency of the query processing in the moving object databases. Therefore, it will be need to adopt parallel processing system for the moving object databases which should maintain the object's current location as precise as possible. This paper proposes a architecture of spatial indexing mobile objects using multiple processors. More precisely, we newly propose a method of splitting buckets using the properties of moving objects in order to minimize the number of database updates. We also propose a acquisition method for gathering the location information of moving objects and passing the information of the bucket extents in order to reduce the amount of passed messages between processors.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.16-23
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• 1998

In this paper, we present a fast and efficient Iterative Improvement Partitioning(IIP) technique for VLSI circuits and hybrid bucket structures on its implementation. The IIP algorithms are very widely used in VLSI circuit partition due to their time efficiency. As the performance of these algorithms depends on choices of moving cell, various methods have been proposed. Specially, Cluster-Removal algorithm by S. Dutt significantly improved partition quality. We indicate the weakness of previous algorithms wjere they used a uniform method for choice of cells during for choice of cells during the improvement. To solve the problem, we propose a new IIP technique that selects the method for choice of cells according to the improvement status and present hybrid bucket structures for easy implementation. The time complexity of proposed algorithm is the same with FM method and the experimental results on ACM/SIGDA benchmark circuits show improvment up to 33-44%, 45%-50% and 10-12% in cutsize over FM, LA-3 and CLIP respectively. Also with less CUP tiem, it outperforms Paraboli and MELO represented constructive-partition methods by about 12% and 24%, respectively.

• 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.