• Journal of KIISE:Databases
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• v.28 no.4
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• pp.596-605
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• 2001

In this paper we propose a method, called the range-based cube partitioning (RCP)method for reducing I/O cost of cube computation in OLAP The method improves I/O performance of cube partitioning process by overlapping some computation between partitioning stages. For overlapping the computation, the method partitions the cube based on the ranges of attribute values, not the points of attribute value, Through analysis any experiments, we show the performance of the proposed method with comparison of the previous cube partitioning method.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.455-464
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• 2012

Due to the recent data explosion, methods which can meet the requirement of large data analysis has been studying. This paper proposes MRIterativeBUC algorithm which enables efficient computation of large data cube by distributed parallel processing with MapReduce framework. MRIterativeBUC algorithm is developed for efficient iterative operation of the BUC method with MapReduce, and overcomes the limitations about the storage size and processing ability caused by large data cube computation. It employs the idea from the iceberg cube which computes only the interesting aspect of analysts and the distributed parallel process of cube computation by partitioning and sorting. Thus, it reduces data emission so that it can reduce network overload, processing amount on each node, and eventually the cube computation cost. The bottom-up cube computation and iterative algorithm using MapReduce, proposed in this paper, can be expanded in various way, and will make full use of many applications.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.84-90
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• 2023

In this article the problem of computing floating-point reciprocal cube root functions is considered. Our new algorithms for this task decrease the number of arithmetic operations used for computing $1/{\sqrt[3]{x}}$. A new approach for selection of magic constants is presented in order to minimize the computation time for reciprocal cube roots of arguments with movable decimal point. The underlying theory enables partitioning of the base argument range x∈[1,8) into 3 segments, what in turn increases accuracy of initial function approximation and decreases the number of iterations to one. Three best algorithms were implemented and carefully tested on 32-bit microcontroller with ARM core. Their custom C implementations were favourable compared with the algorithm based on cbrtf(x) function taken from C ＜math.h＞ library on three different hardware platforms. As a result, the new fast approximation algorithm for the function $1/{\sqrt[3]{x}}$ was determined that outperforms all other algorithms in terms of computation time and cycle count.

• The KIPS Transactions:PartD
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• v.14D no.6
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• pp.597-604
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• 2007

Stream data is a continuous flow of information that mostly arrives as the form of an infinite rapid stream. Recently researchers show a great deal of interests in analyzing such data to obtain value added information. Here, we propose an efficient cube computation algorithm for multidimensional analysis of stream data. The fact that stream data arrives in an unsorted fashion and aggregation results can only be obtained after the last data item has been read. cube computation requires a tremendous amount of memory. In order to resolve such difficulties, we compute user selected aggregation fables only, and use a combination of an way and AVL trees as a temporary storage for aggregation tables. The proposed cube computation algorithm works even when main memory is not large enough to store all the aggregation tables during the computation. We showed that the proposed algorithm is practically fast enough by theoretical analysis and performance evaluation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.3-11
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• 2011

Evaluation of cube roots in characteristic three finite fields is required for Tate (or modified Tate) pairing computation. The Hamming weights (the number of nonzero coefficients) in the polynomial representations of $x^{1/3}$ and $x^{2/3}$ determine the efficiency of cube roots computation, where $F_{3^m}$is represented as $F_3[x]/(f)$ and $f(x)=x^m+ax^k+b{\in}F_3[x]$ (a, $b{\in}F_3$) is an irreducible trinomial. O. Ahmadi et al. determined the Hamming weights of $x^{1/3}$ and $x^{2/3}$ for all irreducible trinomials. In this paper, we present formulas for cube roots in $F_{3^m}$ using the shifted polynomial basis(SPB). Moreover, we provide the suitable shifted polynomial basis bring no further modular reduction process.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.196-204
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• 2012

Recently, many applications perform OLAP(On-Line Analytical Processing) over a very large volume of data. Multidimensional data cube is regarded as a core tool in OLAP analysis. This paper focuses on the method how to efficiently compute data cubes in parallel by using a popular parallel processing tool, MapReduce. We investigate efficient ways to implement PipeSort algorithm, a well-known data cube computation method, on the MapReduce framework. The PipeSort executes several (descendant) cuboids at the same time as a pipeline by scanning one (ancestor) cuboid once, which have the same sorting order. This paper proposed four ways implementing the pipeline of the PipeSort on the MapReduce framework which runs across 20 servers. Our experiments show that PipeMap-NoReduce algorithm outperforms the rest algorithms for high-dimensional data. On the contrary, Post-Pipe stands out above the others for low-dimensional data.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.3
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• pp.351-356
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• 1986

This paper proposes a new algorithm for logic minimization to optimize the area of a PLA chip. All minterms are expressed in the form of decimal number, and sets of minterms which are not included in the essestia cube are deleted prior to cube generation, ther by making cube generation easy. Also, for reduction of computation time, the properties of multioutput functions are considered. That is, only the combinations of functions correcsponding to common minterms are minimized. The proposed algorithm is implemented on VAX 11/780 using Pascal and compared with conventional methods.

• Journal of Information Processing Systems
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• v.7 no.2
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• pp.271-286
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• 2011

In this paper, we propose efficient algorithms for parallel prefix computation and sorting on a recursive dual-net. The recursive dual-net $RDN^k$(B) for k > 0 has $(2n_o)^{2K}/2$ nodes and $d_0$ + k links per node, where $n_0$ and $d_0$ are the number of nod es and the node-degree of the base-network B, respectively. Assume that each node holds one data item, the communication and computation time complexities of the algorithm for parallel prefix computation on $RDN^k$(B), k > 0, are $2^{k+1}-2+2^kT_{comm}(0)$ and $2^{k+1}-2+2^kT_{comp}(0)$, respectively, where $T_{comm}(0)$ and $T_{comp}(0)$ are the communication and computation time complexities of the algorithm for parallel prefix computation on the base-network B, respectively. The algorithm for parallel sorting on $RDN^k$(B) is restricted on B = $Q_m$ where $Q_m$ is an m-cube. Assume that each node holds a single data item, the sorting algorithm runs in $O((m2^k)^2)$ computation steps and $O((km2^k)^2)$ communication steps, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.11
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• pp.479-486
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• 2014

MapReduce is a programing model used for parallelly processing a large amount of data. To analyze a large amount data, the data cube is widely used, which is an operator that computes group-bys for all possible combinations of given dimension attributes. When the number of dimension attributes is n, the data cube computes $2^n$ group-bys. In this paper, we propose an efficient method for computing data cubes using MapReduce. The proposed method partitions $2^n$ group-bys into $_nC_{{\lceil}n/2{\rceil}}$ batches, and computes those batches in stages using ${\lceil}n/2{\rceil}$ MapReduce jobs. Compared to the existing methods, the proposed method significantly reduces the amount of intermediate data generated by mappers, so that the cost of sorting and transferring those intermediate data is reduced significantly. Consequently, the total processing time for computing a data cube is reduced. Through experiments, we show the efficiency of the proposed method over the existing methods.

• International Journal of Concrete Structures and Materials
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• v.10 no.2
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• pp.163-175
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• 2016

This paper presents a revised procedure for computation of double-K fracture parameters of concrete split-tension cube specimen using weight function of the centrally cracked plate of finite strip with a finite width. This is an improvement over the previous work of the authors in which the determination of double-K fracture parameters of concrete for split-tension cube test using weight function of the centrally cracked plate of infinite strip with a finite width was presented. In a recent research, it was pointed out that there are great differences between a finite strip and an infinite strip regarding their weight function and the solution of infinite strip can be utilized in the split-tension specimens when the notch size is very small. In the present work, improved version of LEFM formulas for stress intensity factor, crack mouth opening displacement and crack opening displacement profile presented in the recent research work are incorporated. The results of the double-K fracture parameters obtained using revised procedure and the previous work of the authors is compared. The double-K fracture parameters of split-tension cube specimen are also compared with those obtained for standard three point bend test specimen. The input data required for determining double-K fracture parameters for both the specimen geometries for laboratory size specimens are obtained using well known version of the Fictitious Crack Model.