• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.207-211
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• 2023

Traditional methods for datamining typically assume that the data is small, centralized, memory resident and static. But this assumption is no longer acceptable, because datasets are growing very fast hence becoming huge from time to time. There is fast growing need to manage data with efficient mining algorithms. In such a scenario it is inevitable to carry out data mining in a distributed environment and Frequent Itemset Mining (FIM) is no exception. Thus, the need of an efficient incremental mining algorithm arises. We propose the Distributed Incremental Approximate Frequent Itemset Mining (DIAFIM) which is an incremental FIM algorithm and works on the distributed parallel MapReduce environment. The key contribution of this research is devising an incremental mining algorithm that works on the distributed parallel MapReduce environment.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.2
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• pp.169-177
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• 2019

It is very important to understand the intention of a target ship to prevent collisions in multiple-ship situations. However, considering the intentions of a large number of ships at the same time is a great burden for the officer who must establish a collision avoidance plan. With a distributed algorithm, a ship can exchange information with a large number of target ships and search for a safe course. In this paper, I have applied a Distributed Stochastic Search Algorithm (DSSA), a distributed algorithm, for ship collision avoidance. A ship chooses the course that offers the greatest cost reduction or keeps its current course according to probability and constraints. DSSA is divided into five types according to the probability and constraints mentioned. In this paper, the five types of DSSA are applied for ship collision avoidance, and the effects on ship collision avoidance are analyzed. In addition, I have investigated which DSSA type is most suitable for collision avoidance. The experimental results show that the DSSA-A and B schemes offered effective ship collision avoidance. This algorithm is expected to be applicable for ship collision avoidance in a distributed system.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.874-877
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• 2008

Nowadays the existing data processing systems can only support some simple query for sensor network. It is increasingly important to process the vast data streams in sensor network, and achieve effective acknowledges for users. In this paper, we propose a holistic distributed k-means algorithm for sensor network. In order to verify the effectiveness of this method, we compare it with central k-means algorithm to process the data streams in sensor network. From the evaluation experiments, we can verify that the proposed algorithm is highly capable of processing vast data stream with less computation time. This algorithm prefers to cluster the data streams at the distributed nodes, and therefore it largely reduces redundant data communications compared to the central processing algorithm.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.157-162
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• 2010

We present a new distributed genetic algorithm that can be used to extract useful information from distributed, large data over the network. The main idea of the proposed algorithms is to determine how many and which individuals move between subpopulations at each site adaptively. In addition, we present a method to help individuals from other subpopulations not be weeded out but adapt to the new subpopulation. We used six data sets from UCI Machine Learning Repository to compare the performance of our approach with that of the single, centralized genetic algorithm. As a result, the proposed algorithm produced better performance than the single genetic algorithm in terms of the classification accuracy with the feature subsets.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.501-508
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• 2004

In this paper, a coordinated scheduling algorithm is proposed to reduce end-to-end delay in distributed control of systems. For the algorithm, the analysis of practical end-to-end delay in the worst case is performed priory with considering implementation of the systems. The end-to-end delay is composed of the delay caused by multi-task scheduling of operating systems, the delay caused by network communications, and the delay caused by asynchronous timing between operating systems and network communications. Through some simulation tests based on CAN(Controller Area Network), the proposed worst case end-to-end delay analysis is validated. Through the simulation tests, it is also shown that a real-time distributed control system designed to existing worst case delay cannot guarantee end-to-end time constraints. With the analysis, a coordinated scheduling algorithm is proposed here. The coordinated scheduling algorithm is focused on the reduction of the delay caused by asynchronous timing between operating systems and network communications. Online deadline assignment strategy is proposed for the scheduling. The performance enhancement of the distributed control systems by the scheduling algorithm is shown through simulation tests.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1153-1156
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• 1987

This paper presents a distributed algorithm for deadlock detection in distributed computing system. The deadlock could be detected optimally from cycle. This algorithm maintains the Process-Resource Table and it could be found the deadlock from the table.

• International Journal of Reliability and Applications
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• v.9 no.1
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• pp.1-5
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• 2008

A heuristic algorithm for reliability optimization of a distributed network system is developed so that the reliability of a large system can be determined efficiently. This heuristic bases on the determination of maximal reliability set of maximum node capacity, maximal link reliability and maximal node degree.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.298-305
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• 2010

In this paper, a new algorithm of optimal Volt/Var Control is proposed using Volt/Var control for the Distribution Automation System (DAS) with Distributed Generation (DG) based on the modeling of the distributed load and the distributed current. In the proposed, algorithm based on the modeling of the distributed load and the distributed current are estimated from constants of four terminals using the measurement of the current and power factor from a Feeder Remote Terminal Unit (FRTU) and DG data from RTU for DG. For the optimal Volt/Var Control, the gradient method is applied to find optimal solution for tap, capacity and power control of OLTC (On-Load Tap Changers), SVR (Step Voltage Regulator), PC (Power Condenser) and DG (Distributed Generation). In the case studies, the estimation and control of the voltages have been testified in a radial distribution system with DG using matlab program.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.246-251
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• 1998

The basic requirement of a distributed multimedia system are intramedia synchronization which asks the strict delay and jitter for the check period of media buffer and the scaling duration with periodic continuous media such as audio and video media, and intermedia synchronization that needs the constraint for relative time relations among them when several media are presented in parallel. In this paper, a distributed multimedia synchronization algorithm based on the fuzzy logic is presented and the performance is evaluated through simulation. Intramedia synchronisation algorithm uses the media scaling techniques and intermedia synchronization algorithm uses variable service rates on the basis of fuzzy logic to solve the multimedia synchronization problem.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1259-1269
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• 2012

Cloud computing has rapidly become a new infrastructure for organizations to reduce their capital cost in IT investment and to develop planetary-scale distributed applications. One of the fundamental challenges in geographically distributed clouds is to provide efficient algorithms for supporting inter-cloud data management and dissemination. In this paper, we propose a geographic group quorum system (GGQS)-based hybrid algorithm for improving the interoperability of inter-cloud in time-critical event dissemination service, such as computing policy updating, message sharing, event notification and so forth. The proposed algorithm first organizes these distributed clouds into a geographic group quorum overlay to support a constant event dissemination latency. Then it uses a hybrid protocol that combines geographic group-based broad-cast with quorum-based multicast. Our numerical results show that the GGQS-based hybrid algorithm improves the efficiency as compared with Chord-based, Plume an GQS-based algorithms.