• 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.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.188-191
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• 2003

In this paper, we proposed data generalization and data cube mechanism for efficient data mining in distribute environment. We also proposed active Self Organization Map applying traditional Self Organization Map of Neural network for searching the most Informative data created from data cube after the generalization procedure and designed the system architecture for that.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6161-6176
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• 2018

Pseudorandom numbers are useful in cryptographic operations for using as nonce, initial vector, secret key, etc. Security of the cryptosystem relies on the secret key parameters, so a good pseudorandom number is needed. In this paper, we have proposed a new approach for generation of pseudorandom number. This method uses the three dimensional combinational puzzle Rubik Cube for generation of random numbers. The number of possible combinations of the cube approximates to 43 quintillion. The large possible combination of the cube increases the complexity of brute force attack on the generator. The generator uses cryptographic hash function. Chaotic map is being employed for increasing random behavior. The pseudorandom sequence generated can be used for cryptographic applications. The generated sequences are tested for randomness using NIST Statistical Test Suite and other testing methods. The result of the tests and analysis proves that the generated sequences are random.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.25-26
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• 2010

대용량 데이터의 효율적 분석을 위해 데이터 뷰브가 연구되었으며, 데이터 큐브 계산의 고비용 문제점을 해결하기 위하여 큐브의 일부 영역만을 계산하는 빙산 큐브가 등장하였다. 빙산 큐브는 저장 공간의 감소, 집중적인 분석 등의 장점이 있으나, 여전히 많은 계산과 저장 공간을 필요로 하는 단점이 있다. 본 논문에서는 이러한 문제점을 해결하는 실용적인 방법으로 대용량 문제를 분산하여 처리하는 분산 병렬 컴퓨팅 기술인 맵리듀스(MapReduce) 프레임워크를 사용하여 분산 병렬 빙산 큐브인 MR-Naive와 MR-BUC 알고리즘을 제안한다. 실험을 통해 맵리듀스 프레임워크를 통한 빙사 큐브 계산이 효율적으로 분산 병렬 처리 됨을 확인하였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.3-12
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• 2011

A hashing function that maps arbitrary messages directly onto curve points (MapToPoint) has non-negligible complexity in pairing-based cryptosystems. Unlike elliptic curve cryptosystems, pairing-based cryptosystems require the hashing function in ternary fields. Barreto et al. observed that it is more advantageous to hash the message to an ordinate instead of an abscissa. So, they significantly improved the hashing function by using a matrix with coefficients of the abscissa. In this paper, we improve the method of Barreto et al. by reducing the matrix. Our method requires only 44% memory of the previous result. Moreover we can hash a message onto a curve point 2~3 times faster than Barreto's Method.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1457-1465
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• 2019

3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.6
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• pp.1333-1337
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• 2011

3D engines loading, and expansion of the usable capacity, next-generation smartphone game markets are rising briskly by the improvement in CPU processing speed of Phones (hardware of smartphone). Therefore, in creating 3D game characters, realistic and free-form animations in a small screen of a smartphone are becoming important. Through this paper, as a method of creating characters and operating for game characters to cause user's feeling, with NURBS data of MAYA, We completed a face in turns of eyes, a nose, and a mouth, and with Polygon Cube tool, modeled hands and feet. After dividing a cube into half and modeling it, through mirror copying We completed the whole body and modeled the low-polygon. Then to model realistic and free-form characters, We completed each detail with ZBrush and applied Divide level up to 4. Though they might look rough and exaggerated, We tried to express stuck-out parts and fallen-in parts effectively and smoothly with Smooth brush effect, map and design the low-polygon 3D characters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.300-303
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• 2011

Owing to the improvement in CPU processing speed of Phones (hardware of smartphone), 3D engines loading, and expansion of the usable capacity, next-generation smartphone game markets are rising briskly. Therefore, in creating 3D game characters, realistic and free-form animations in a small screen of a smartphone are becoming important. Through this paper, as a method of creating characters and operating for game characters to cause user's feeling, with NURBS data of MAYA, We completed a face in turns of eyes, a nose, and a mouth, and with Polygon Cube tool, modeled hands and feet. After dividing a cube into half and modeling it, through mirror copying We completed the whole body and modeled the low-polygon. Then to model realistic and free-form characters, We completed each detail with ZBrush and applied Divide level up to 4. Though they might look rough and exaggerated, We tried to express stuck-out parts and fallen-in parts effectively and smoothly with Smooth brush effect, map and design the low-polygon 3D characters.