• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.125-132
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• 2014

Since frequent pattern mining was proposed in order to search for hidden, useful pattern information from large-scale databases, various types of mining approaches and applications have been researched. Especially, frequent graph pattern mining was suggested to effectively deal with recent data that have been complicated continually, and a variety of efficient graph mining algorithms have been studied. Graph patterns obtained from graph databases have their own importance and characteristics different from one another according to the elements composing them and their lengths. However, traditional frequent graph pattern mining approaches have the limitations that do not consider such problems. That is, the existing methods consider only one minimum support threshold regardless of the lengths of graph patterns extracted from their mining operations and do not use any of the patterns' weight factors; therefore, a large number of actually useless graph patterns may be generated. Small graph patterns with a few vertices and edges tend to be interesting when their weighted supports are relatively high, while large ones with many elements can be useful even if their weighted supports are relatively low. For this reason, we propose a weight-based frequent graph pattern mining algorithm considering length-decreasing support constraints. Comprehensive experimental results provided in this paper show that the proposed method guarantees more outstanding performance compared to a state-of-the-art graph mining algorithm in terms of pattern generation, runtime, and memory usage.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10c
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• pp.191-193
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• 1999

대 규모의 CORBA 시스템을 구축할 때 서로 다른 프로토콜을 갖는 여러 개의 ORB 도메인이 존재한다. 이러한 서로 다른 ORB 도메인들 간의 통신은 ORB 브리지를 통하여 수행된다. 따라서 전체 시스템에서 각 ORB 도메인 간의 브리지의 수는 가능하면 적어야 하고 각 브리지에 가중되는 부하 역시 적어야 한다. 본 논문에서는 이러한 문제를 그래프 개념으로 모델링하고 기본적인 그래프 연산들을 이용하여, 브리지의 수를 줄이고 각 브리지에 가중되는 부하를 균등하게 분할하여 전체 시스템을 구성하는 효율적인 알고리즘을 제안한다.

• Water for future
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• v.51 no.6
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• pp.51-56
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• 2018

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.93-100
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• 2021

The Ethereum shard system for solving the scalability problem of the blockchain has a load balancing issue, which is modeled as a graph partitioning problem. In this paper, we propose an adaptive online weighted graph partitioning algorithm that can negotiate between two utility of the shard system using the game theory's bargaining solution. The bargaining solution is an axiomatic solution that can fairly determine the points of conflict of utility. The proposed algorithm was improved to apply the existing online graph partitioning algorithm to the weighted graph, and load balancing was performed efficiently through the design considering the situation of the sharding system using the extension of Nash bargaining solution, which is extended to apply solution to non-convex feasible set of bargaining problem. As a result of the experiment, it showed up to 37% better performance than typical load balancing algorithm of shard system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.143-152
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• 2013

Given weighted graph G=(V,E), n=|V|, m=|E|, the minimum cut problem is classified with source s and sink t or without s and t. Given undirected weighted graph without s and t, Stoer-Wagner algorithm is most popular. This algorithm fixes arbitrary vertex, and arranges maximum adjacency (MA)-ordering. In the last, the sum of weights of the incident edges for last ordered vertex is computed by cut value, and the last 2 vertices are merged. Therefore, this algorithm runs $\frac{n(n-1)}{2}$ times. Given graph with s and t, Ford-Fulkerson algorithm determines the bottleneck edges in the arbitrary augmenting path from s to t. If the augmenting path is no more exist, we determine the minimum cut value by combine the all of the bottleneck edges. This paper suggests minimum cut algorithm for undirected weighted graph with s and t. This algorithm suggests MA-merging and computes cut value simultaneously. This algorithm runs n-1 times and successfully divides V into disjoint S and V sets on the basis of minimum cut, but the Stoer-Wagner is fails sometimes. The proposed algorithm runs more than Ford-Fulkerson algorithm, but finds the minimum cut value within n-1 processing times.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.281-285
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• 2008

Wireless sensor networks are closely related with the geometric environment in which they are deployed. We consider the probable case when a routing protocol runs on an environment with many complex obstacles like downtown surroundings. In addition, there are no unrealistic assumptions in order to increase practicality of the protocol. Our goal is to find a routing protocol for maximizing network lifetime by using only connectivity information in the complex sensor network environment. We propose a topology-based routing algorithm that accomplishes good performance in terms of network lifetime and routing complexity as measures. Our routing algorithm makes routing decision based on a weighted graph as topological abstraction of the complex network. The graph conduces to lifetime enhancement by giving alternative paths, distributing the skewed burden. An energy estimation method is used so as to maintain routing information without any additional cost. We show how our approach can be used to maximize network lifetime and by extensive simulation we prove that out approach gives good results in terms of both measures-network lifetime and routing complexity.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.24-26
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• 2000

본 논문에서는 FDS를 응용한 하드웨어 분할 방법을 강 제약 조건을 만족하면서 FDS를 응용하는 방법보다 낮은 복잡도의 분할 알고리즘을 제안한다. 기존의 FDS 응용 방법은 힘값 계산에서 종속성에 의해 후위 연산이 받는 영향값을 계산하여야 하므로 이로 인한 시간 복잡도가 가중되었다. 본 논문에서는 이러한 복잡도를 저하시키기 위해 노드의 분포 그래프와 구현에 소요되는 비용, 그리고 해당 파티션에서의 실행시간 등에 의해 상대적 긴박도를 정의하여 분할을 수행하지만, 종속성 검사는 종속성 제약조건에 의한 분포그래프의 변화와 스케쥴에 대해서만 고려되며 힘값 계산에는 고려하지 않는다. 또한, 분할 단계에서 스케쥴링을 함께 고려함으로써 합성 이후에 재 스케쥴링의 부하를 경감할 수 있도록 하였다. 제안 알고리즘 결과는 ILP 결과와 비교 분석하였다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.17-29
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• 1999

This paper presents a high level synthesis method targeting low power consumption for data-dominated CMOS circuits (e.g., DSP). The high level synthesis is divided into three basic tasks: scheduling, resource and register allocation. For lower power scheduling, we increase the possibility of reusing an input operand of functional units. For a scheduled data flow graph, a compatibility graph for register and resource allocation is formed, and then a special weighted network is then constructed from the compatibility graph and the minimum cost flow algorithm is performed on the network to obtain the minimum power consumption data path assignment. The formulated problem is then solved optimally in polynomial time. This method reduces both the switching activity and the capacitance in synthesized data path. Experimental results show 15% power reduction in benchmark circuits.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.31-39
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• 2015

The degree-constrained minimum spanning tree (d-MST) problem is considered NP-complete for no exact solution-yielding polynomial algorithm has been proposed to. One thus has to resort to an heuristic approximate algorithm to obtain an optimal solution to this problem. This paper therefore presents a polynomial time algorithm which obtains an intial solution to the d-MST with the help of Kruskal's algorithm and performs k-opt on the initial solution obtained so as to derive the final optimal solution. When tested on 4 graphs, the algorithm has successfully obtained the optimal solutions.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.237-251
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• 2023

This study explores the possibility of automated scoring for scientific graph answers by designing an automated scoring model using convolutional neural networks and applying it to students' kinematics graph answers. The researchers prepared 2,200 answers, which were divided into 2,000 training data and 200 validation data. Additionally, 202 student answers were divided into 100 training data and 102 test data. First, in the process of designing an automated scoring model and validating its performance, the automated scoring model was optimized for graph image classification using the answer dataset prepared by the researchers. Next, the automated scoring model was trained using various types of training datasets, and it was used to score the student test dataset. The performance of the automated scoring model has been improved as the amount of training data increased in amount and diversity. Finally, compared to human scoring, the accuracy was 97.06%, the kappa coefficient was 0.957, and the weighted kappa coefficient was 0.968. On the other hand, in the case of answer types that were not included in the training data, the s coring was almos t identical among human s corers however, the automated scoring model performed inaccurately.