• 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 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.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.519-523
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• 2010

In Gen-2 RFID system, the initial value of $Q_{fp}$, which is the slot-count parameter of Q-algorithm, is not defined in the standard. In this case, if the number of tags within the reader's identification range is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to allocate the optimal initial $Q_{fp}$ through the tag number estimation before the query round begins. Through computer simulations, it is demonstrated that the proposed scheme achieves more stable performance than Gen-2 Q-algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.723-725
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• 2016

In Gen-2 Q-algorithm, if the number of tags is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to select the weight based on the slot-count size of current query round through the tag number estimation and.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.470-475
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• 2012

In Q-algorithm of EPCglobal Class-1 Gen-2 RFID system, the initial value of $Q_{fp}$, which is the slot-count parameter, is not defined in the standard. And the values of weight C, which is the parameter for incrementing or decrementing the slot-count size, are not determined. Therefore, if the number of tags is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to allocate the optimal initial $Q_{fp}$ through the tag number estimation and select the weight based on the slot-count size of current query round.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1011-1020
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• 2004

A lot of work has been done to improve the I/O performance of such a system that store and manage a massive amount of data by distributing them across multiple disks and access them in parallel. Most of the previous work has focused on an efficient mapping from a grid ceil, which is determined bY the interval number of each dimension, to a disk number on the assumption that each dimension is split into disjoint intervals such that entire data space is GRID-like partitioned. However, they have ignored the effects of a GRID partitioning scheme on declustering performance. In this paper, we enhance the performance of mapping function based declustering algorithms by applying a good GRID par-titioning method. For this, we propose an estimation model to count the number of grid cells intersected by a range query and apply a GRID partitioning scheme which minimizes query result size among the possible schemes. While it is common to do binary partition for high-dimensional data, we choose less number of dimensions than needed for binary partition and split several times along that dimensions so that we can reduce the number of grid cells touched by a query. Several experimental results show that the proposed estimation model gives accuracy within 0.5% error ratio regardless of query size and dimension. We can also improve the performance of declustering algorithm based on mapping function, called Kronecker Sequence, which has been known to be the best among the mapping functions for high-dimensional data, up to 23 times by applying an efficient GRID partitioning scheme.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.379-388
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• 2007

Disordered streams may cause inaccurate or delayed results in window-based queries. Existing approaches usually leverage buffers to hand]e the streams. However, most of the approaches estimate the buffer size simply based on the maximum network delay in the streams, which tends to over-estimate the buffer size and result in high latency. In this paper, we propose a probabilistic approach to estimate the buffer size adaptively according to the fluctuated network delays. We first assume that intervals of tuple generations follow an exponential distribution and network delays have a normal distribution. Then, we derive an estimation function from the assumptions. The function takes a drop ratio as an input parameter, which denotes a percentage of tuple drops permissible during query execution. By describing the drop ratio in a query specification, users can control the quality of query results such as accuracy or latency according to application requirements. Our experimental results show that the proposed function has better adaptivity than the existing function based on the maximum network delay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5888-5903
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• 2019

The Relational ranking method applies authority-based ranking in relational dataset that can be modeled as graphs considering also their tuples' values. Authority directions from tuples that contain the given keywords and transfer to their corresponding neighboring nodes in accordance with their values and semantic connections. From our previous work, ObjectRank extends to ValueRank that also takes into account the value of tuples in authority transfer flows. In a maked difference from ObjectRank, which only considers authority flows through relationships, it is only valid in the bibliographic databases e.g. DBLP dataset, ValueRank facilitates the estimation of importance for any databases, e.g. trading databases, etc. A relational keyword search paradigm Object Summary (denote as OS) is proposed recently, given a set of keywords, a group of Object Summaries as its query result. An OS is a multilevel-tree data structure, in which node (namely the tuple with keywords) is OS's root node, and the surrounding nodes are the summary of all data on the graph. But, some of these trees have a very large in total number of tuples, size-l OSs are the OS snippets, have also been investigated using ValueRank.We evaluated the real bibliographical dataset and Microsoft business databases to verify of our proposed approach.