• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.4
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• pp.790-794
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• 2007

This paper presents a new index mapping method for DFT (Discrete Fourier Transform) and its application to multidimensional DFT. Unlike conventional index mapping methods such as DIT (Decimation in Time) or DIF (Decimation in Frequency) algorithms, the proposed method is based on two levels of decomposition and it can be very efficiently used for implementing multidimensional DFT as well as 1-dimensional DFT. The proposed pipelined architecture for multidimensional DFT is very flexible so that it can lead to the best tradeoff between performance and hardware requirements. Also, it can be easily extended to higher dimensional DFTs since the number of CEs (Computational Elements) and DCs (Delay Commutators) increase only linearly with the dimension. Various implementation options based on different radices and different pipelining depths will be presented.

• International Journal of Contents
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• v.1 no.1
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• pp.50-58
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• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts 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 multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. 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.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.7
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• pp.925-934
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• 2017

R-tree is index structure which is frequently used for handling spatial data. However, if the number of dimensions increases, or if only partial dimensions are used for searching the certain data according to user preference, the time for indexing is greatly increased and the efficiency of the generated R-tree is greatly reduced. Hence, it is not suitable for the multidimensional data, where dimensions are continuously increasing. In this paper, we propose a multidimensional hash index, a new multidimensional index structure based on a hash index. The multidimensional hash index classifies data into buckets of euclidean space through a hash function, and then, when an actual search is requested, generates a hash search tree for effective searching. The generated hash search tree is able to handle user preferences in selected dimensional space. Experimental results show that the proposed method has better indexing performance than R-tree, while maintaining the similar search performance.

• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.269-281
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• 2011

For the MD-TIX(multidimensional type inheritance index) that supports query processing for the type inheritance concept in XML databases, this paper presents an index tuning algorithm that enhances the performance of the XML query processing according to the query pattern. The MD-TIX uses a multidimensional index structure to support complex XML queries involving both nested elements and type inheritance hierarchies. In this index tuning algorithm, we first determine a shape of index page regions by using the query information about the user's query pattern, and then construct an optimal MD-TIX by applying a region splitting strategy that makes the shape of the page regions into the predetermined one. The performance evaluation results indicate that the proposed tuning algorithm builds an optimal MD-TIX by a given query pattern, and in the case of the three-dimensional query regions for the nested predicates of path length 2, the performance is much enhanced according to the skewed degree of the query region's shape.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.225-236
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• 2003

MOLAP systems store data in a multidimensional away called a 'cube' and access them using way indexes. When a cube is placed into disk, it can be Partitioned into a set of chunks of the same side length. Such a cube storage scheme is called the chunk-based MOLAP cube storage scheme. It gives data clustering effect so that all the dimensions are guaranteed to get a fair chance in terms of the query processing speed. In order to achieve high space utilization, sparse chunks are further compressed. Due to data compression, the relative position of chunks cannot be obtained in constant time without using indexes. In this paper, we propose a bitmap index for chunk-based MOLAP cubes. The index can be constructed along with the corresponding cube generation. The relative position of chunks is retained in the index so that chunk retrieval can be done in constant time. We placed in an index block as many chunks as possible so that the number of index searches is minimized for OLAP operations such as range queries. We showed the proposed index is efficient by comparing it with multidimensional indexes such as UB-tree and grid file in terms of time and space.

• Journal of applied mathematics & informatics
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• v.40 no.1_2
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• pp.289-303
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• 2022

This article examines the multidimensional index extraction method of the disability social security system based on data mining. While creating the data warehouse of the social security system for the disabled, we need to know the elements of the social security indicators for the disabled. In this context, a clustering algorithm was used to extract the indicators of the social security system for the disabled by investigating the historical dimension of social security for the disabled. The simulation results show that the index extraction method has high coverage, sensitivity and reliability. In this paper, a multidimensional extraction method is introduced to extract the indicators of the social security system for the disabled based on data mining. The simulation experiments show that the method presented in this paper is more reliable, and the indicators of social security system for the disabled extracted are more effective in practical application.

• Korean Journal of Organic Agriculture
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• v.29 no.4
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• pp.471-484
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• 2021

The characteristics of poverty can be comprehensively revealed from the two angles of income and multidimensional. This paper compares China's rural income poverty measure with multidimensional poverty index using data from China Family Panel Studies (CFPS) by focusing on the static and dynamic disparities, and analyzes the factors influencing poverty through the Logit model. The results show that there exists a substantial mismatch in who is deemed poor, 60 percent of multidimensional poverty households are not considered poor in terms of income poverty, and 70 percent of income poverty households are not considered poor in terms of multidimensional poverty; There is a high level of disparity between the dynamics of the two measures of poverty. Among those who rose in the income dimension, only about 7 percent also rose in the multidimensional measure from 2016 to 2018.

• East Asian Economic Review
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• v.27 no.3
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• pp.243-269
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• 2023

The multidimensional poverty index is an indicator system established for defining and evaluating poverty, to understand poverty in dimensions beyond just monetary scarcity. Based on income, education, health, living standards, and social dimensions, this article measures and analyzes the level of multidimensional poverty in Xinjiang using the AlkireFoster method, with cross-sectional data obtained from a 2022 survey. Probit model is constructed for regression analysis, further considering the impact of education on enhancing feasible capabilities and alleviating multidimensional poverty at the post-poverty alleviation era. The data shows that many people still face significant challenges from the perspective of multidimensional poverty; the decomposition results of each dimension show that education contributes more to the multidimensional poverty; the regression analysis results show that the higher the education level, the lower the multidimensional poverty; heterogeneity analysis revealed that the inhibitory effect of education on multidimensional poverty is greater for females than males, and the poverty reduction effect of education mainly concentrates on middle-aged and older individuals. This article is meaningful for exploring strategies to alleviate multidimensional poverty in ethnic minority regions in frontier areas in the new era, accelerating regional economic development, and achieving shared prosperity.

• Journal of KIISE:Databases
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• v.32 no.3
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• pp.254-262
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• 2005

This paper presents a new sequential indexing method called segment-page indexing (SP-indexing) for multidimensional range queries. The design objectives of SP-indexing are twofold:(1) improving the range query performance of multidimensional indexing methods (MIMs) and (2) providing a compromise between optimal index clustering and the full index reorganization overhead. Although more than ten years of database research has resulted in a great variety of MIMs, most efforts have focused on data-level clustering and there has been less attempt to cluster indexes. As a result, most relevant index nodes are widely scattered on a disk and many random disk accesses are required during the search. SP-indexing avoids such scattering by storing the relevant nodes contiguously in a segment that contains a sequence of contiguous disk pages and improves performance by offering sequential access within a segment. Experimental results demonstrate that SP-indexing improves query performance up to several times compared with traditional MIMs using small disk pages with respect to total elapsed time and it reduces waste of disk bandwidth due to the use of simple large pages.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.315-327
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• 2007

The performance of database applications with large sets of multidimensional data depends on the performance of its access methods and the underlying disk system. In modeling the disk system, even though modem disks are manufactured with multiple physical zones, conventional access methods have been developed based on a traditional disk model with many simplifying assumptions. Thus, there is a marked lack of investigation on how to enhance the performance of access methods given a zoned disk model. The paper proposes novel zoning techniques that can be applied to any multidimensional access methods, both static and dynamic, enhancing the effective data transfer rate of underlying disk system by fully utilizing its zone characteristics. Our zoning techniques include data placement algorithms for multidimensional index structures and accompanying localized query processing algorithms for range queries. The experimental results show that our zoning techniques significantly improve the query performance.