• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.753-763
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• 1999

The existing SCADA(Supervisory Control and Data Acquisition) system software is usually developed to suitable for the specific hardware platforms. However, as per rapid improvement of computer performance and development of network technology, it is required to support scalability and inter-operability in existing different SCADA systems. In order to meet such requirements, in this paper, we propose a new type of SCADA testbed using Java for electric distribution applications. The system consists of three modules; development support tools, client and server modules. The basic architecture of the proposed SCADA system is similar to existing one, however, we improve the function of MTU and MMI interface to facilitate LAN and WAN environment. Also, the proposed system can deals with alarm and history data by using heterogeneous DBMS. Since the system is built in Java environment, the development cost is cheap and it can support sacalability and portability. Our experience can be utilized to develop next generation of small and medium size of SCADA system.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.1-20
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• 2013

Over the past generation, data warehousing and online analytical processing (OLAP) applications have become the cornerstone of contemporary decision support environments. Typically, OLAP servers are implemented on top of either proprietary array-based storage engines (MOLAP) or as extensions to conventional relational DBMSs (ROLAP). While MOLAP systems do indeed provide impressive performance on common analytics queries, they tend to have limited scalability. Conversely, ROLAP's table oriented model scales quite nicely, but offers mediocre performance at best relative to the MOLAP systems. In this paper, we describe a storage and indexing framework that aims to provide both MOLAP like performance and ROLAP like scalability by essentially combining some of the best features from both. Based upon a combination of R-trees and bitmap indexes, the storage engine has been integrated with a robust OLAP query engine prototype that is able to fully exploit the efficiency of the proposed storage model. Specifically, it utilizes an OLAP algebra coupled with a domain specific query optimizer, to map user queries directly to the storage and indexing framework. Experimental results demonstrate that not only does the design improve upon more naive approaches, but that it does indeed offer the potential to optimize both query performance and scalability.

• Journal of Internet Computing and Services
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• v.5 no.6
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• pp.1-10
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• 2004

A web robot is a software that has abilities of tracking and collecting web documents on the Internet(l), The performance scalability of recent web robots reached the limit CIS the number of web documents on the internet has increased sharply as the rapid growth of the Internet continues, Accordingly, it is strongly demanded to study on the performance scalability in searching and collecting documents on the web. 'Design of web robot based on Multi-Agent to speed up documents collection ' rather than 'Sequentially executing Web Robot based on the existing Fork-Join method' and the results of analysis on its performance scalability is presented in the thesis, For collection speedup, a Multi-Agent based web robot performs the independent process for inactive URL ('Dead-links' URL), which is caused by overloaded web documents, temporary network or web-server disturbance, after dividing them into each agent. The agents consist of four component; Loader, Extractor, Active URL Scanner and inactive URL Scanner. The thesis models a Multi-Agent based web robot based on 'Amdahl's Law' to speed up documents collection, introduces a numerical formula for collection speedup, and verifies its performance improvement by comparing data from the formula with data from experiments based on the formula. Moreover, 'Dynamic URL Partition algorithm' is introduced and realized to minimize the workload of the web server by maximizing a interval of the web server which can be a collection target.

• Journal of Korea Spatial Information System Society
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• v.8 no.3
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• pp.105-115
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• 2006

A sensor network which uses DCS(Data-Centric Storage) stores the same data into the same sensor node. Thus it has a hot spot problem when the sensor network grows and the same data arise frequently. In the past researches of the sensor network using DCS, the hot spot problem caused by growing the sensor network was ignored because they only concentrated on managing stored sensor data efficiently. In this paper, we proposed a non-equal region split method that supports efficient scalability on storing multi-dimensional sensor data. This method can reduce the storing cost, as the sensor network is growing, by dividing whole space into regions which have the same number of sensor nodes according to the distribution of sensor nodes, and storing and managing sensor data within each region. Moreover, this method can distribute the energy consumption of sensor nodes by increasing the number of regions according to the size of the sensor network, the number of sensor nodes within the sensor network, and the quantity of sensor data. Therefore it can help to increase the life time and the scalability of the sensor network.

• Proceedings of the KSEEG Conference
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• 2002.04a
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• pp.146-149
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• 2002

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.621-628
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• 2005

Scalability of clustering algorithms is critical issues facing the data mining community. This is particularly true for computationally intense tasks such as data clustering. Random sampling of instances is one possible means of achieving scalability but a pervasive problem with this approach is how to deal with the noise that this introduces in the evaluation of the learning algorithm. This paper develops a new optimization based clustering approach using an algorithms specifically designed for noisy performance. Numerical results illustrate that with this algorithm substantial benefits can be achieved in terms of computational time without sacrificing solution quality.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.47-53
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• 2017

Recently, huge datasets have been generating rapidly in a variety of fields. Then, there is an urgent need for technologies that will allow efficient and effective processing of huge datasets. Therefore the problems of partitioning a huge dataset effectively and alleviating the processing overhead of the partitioned data efficiently have been a critical factor for scalability and performance in distributed database system. In our work we utilized multicore servers to provide scalable service to our distributed system. The partitioning of database over multicore servers have emerged from a need for new architectural design of distributed database system from scalability and performance concerns in today's data deluge. The system allows uniform access through a web service interface to concurrently distributed databases over multicore servers, using SQMD (Single Query Multiple Database) mechanism based on publish/subscribe paradigm. We will present performance results with the distributed database system built on multicore server, which is time intensive with traditional architectures. We will also discuss future works.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.143-146
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• 2015

Software Defined Network (SDN) separates control plane and data plane to achieve benefits such as centralized management, centralized provisioning, lower device cost and more flexibility. In SDN, scalability is an important issue. Centralized controller can be a bottle neck and many research tried to solve this issue on the control plan. However, scalability issue does not only happen in the control plane, but also happen in the data plane. In the data plane, flow table is an important component and its size is limited. In a large network operated by SDN technology, the performance of the network can be highly degraded because of the size limitation of a flow table. In this paper, we propose a ranking-based flow replacement method, Flow Table Management (FTM), to overcome this problem.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.290-295
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• 2023

The global warming by greenhouse gases causes climate change and disasters such as earthquakes and tsunamis frequently, leading to great damage. It is important to build efficient and scalable disaster response services to minimize the damage. Existing disaster warning service by the mobile text is limited by the scalability and the data size to be delivered. In this paper, we propose a framework for disaster response services that is efficient and flexible by allowing to adopt various indexing schemes and scalable by supporting any number of clients in disaster situations anytime and anywhere. Also, the framework by wireless data broadcast can be free from the limitation of the size of data to be delivered. We design and implement the proposed framework and evaluate the framework. For the evaluation, we simulate the implemented framework by adopting various indexing schemes like HCI, DSI and TTSI, and by comparing the access times of the clients. Through the evaluation, we show that the proposed framework can provide efficient and scalable and flexible disaster response services.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.497-504
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• 2018

This paper studies about the Oriental Medical Expert System, based on Open Source Drools for rule engine processing, which contains scalability, availability, and modifiability. The system is developed with the Spring MVC framework and Ajax for stable services of the Web-based Medical Expert System. The diagnosis and treatment process of this Medical Expert system provides a service that provides the general users to accesses the web with a series of questionnaires. In order to compensate for the asynchronous communication between clients and services, and also for the complicated JDBC weaknesses, we applied the data handling in JSON to reduce the servers' loads, and also the Mybatis framework to improve the performance of the RDBMS, respectively. In addition, as the number of users increases to cope with the maximum available services of the web-based system, the load balancing structure using Nginx has been developed to solve the server traffic problems and the service availability has been increased. The experimental results show the stable services by approving the scalability test.