• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1213-1220
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• 2004

With the significant advances in mobile computing technology, there is an increasing demand for various mobile applications to process trans-actions in a real-time. When remote data access is considered in a mobile environment, data access delay becomes one of the most serious problems in meeting the deadline of real-time transaction. The mobile real-time transaction should be assured not only correctness of result of trans-action but also completion time of transaction. In this paper, we propose an optimistic concurrency control method to solve conflict among mobile real-time transactions. It minimizes influence on the cascade abort and delay of transactions that occur by disconnection and hand over in a mobile environment.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.29-34
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• 2001

In this paper, a new associative memory system for analog time-sequential data processing is proposed. This system effectively associate time-sequential data using not only matching with present data but also matching with past data. Furthermore in order to improve error correction ability, weight varying in time domain is introduced in this system. The network is simulated with several periodic time-sequential input patterns including noise. The results show that the proposed system has ability to correct input errors. We expect that the proposed system may be applied for a real time processing of analog time-sequential information.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.635-652
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• 2011

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.13-22
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• 2017

This research proposes and applies a pipelining parallel processing technique to enhance the speed of visualizing the results of real-time simulations. Generally, a simulation with real-time visualization consists of three processes: executing a simulation model, transmitting simulation result, and visualizing simulation result. If we have these processes in serial, the latency from simulation to visualization will be very long, which degrades the speed of visualization of data from real-time simulation. Thus, the main purpose of this research is maximizing performance by adapting pipelining parallel processing technique to the real-time simulation visualization. Also we show that performance is improved by adding multi-threading technique to each process. This paper proposes a theoretical performance model and simulation results of the techniques and then we applied this to an air combat simulation model as a case study. As the result, it shows that the performance is greatly enhanced than the original model's execution time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5271-5289
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• 2016

On large-scale data analysis platforms deployed on cloud infrastructures over the Internet, the instability of the data transfer time and the dynamics of the processing rate require a more sophisticated data distribution scheme which maximizes parallel efficiency by achieving the balanced load among participated computing elements and by eliminating the idle time of each computing element. In particular, under the constraints that have the real-time and limited data buffer (in-memory storage) are given, it needs more controllable mechanism to prevent both the overflow and the underflow of the finite buffer. In this paper, we propose an auto regulated data provisioning model based on receiver-driven data pull model. On this model, we provide a synchronized data replenishment mechanism that implicitly avoids the data buffer overflow as well as explicitly regulates the data buffer underflow by adequately adjusting the buffer resilience. To estimate the optimal size of buffer resilience, we exploits an adaptive buffer resilience control scheme that minimizes both data buffer space and idle time of the processing elements based on directly measured sample path analysis. The simulation results show that the proposed scheme provides allowable approximation compared to the numerical results. Also, it is suitably efficient to apply for such a dynamic environment that cannot postulate the stochastic characteristic for the data transfer time, the data processing rate, or even an environment where the fluctuation of the both is presented.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.56-68
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• 2022

Recently, as the demand for real-time processing increases, studies on a dynamic graph that changes over time has been actively done. There is a connected components processing algorithm as one of the algorithms for analyzing dynamic graphs. GPUs are suitable for large-scale graph calculations due to their high memory bandwidth and computational performance. However, when computing the connected components of a dynamic graph using the GPU, frequent data exchange occurs between the CPU and the GPU during real graph processing due to the limited memory of the GPU. The proposed scheme utilizes the Weighted-Quick-Union algorithm to process large-scale graphs on the GPU. It supports fast connected components computation by applying the size to the connected component label. It computes the connected component by determining the parts to be recalculated and minimizing the data to be transmitted to the GPU. In addition, we propose a processing structure in which the GPU and the CPU execute asynchronously to reduce the data transfer time between GPU and CPU. We show the excellence of the proposed scheme through performance evaluation using real dataset.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1402-1408
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• 2017

Recently, services for personal biometric data analysis based on real-time monitoring systems has been increasing and many of them have focused on recognition of emotions. In this paper, we propose a classification model to classify anxiety emotion using biometric data actually collected from people. We propose to deploy the support vector machine to build a classification model. In order to improve the classification accuracy, we propose two data pre-processing procedures, which are normalization and data deletion. The proposed algorithms are actually implemented based on Real-time Traffic Flow Measurement structure, which consists of data collection module, data preprocessing module, and creating classification model module. Our experiment results show that the proposed classification model can infers anxiety emotions of people with the accuracy of 65.18%. Moreover, the proposed model with the proposed pre-processing techniques shows the improved accuracy, which is 78.77%. Therefore, we can conclude that the proposed classification model based on the pre-processing process can improve the classification accuracy with lower computation complexity.

• Journal of Information Technology Services
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• v.10 no.1
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• pp.135-149
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• 2011

In this paper, we propose the high-risk group detection model for patients with high blood pressure using case-based reasoning. The proposed model can be applied for public health maintenance organizations to effectively manage knowledge related to high blood pressure and efficiently allocate limited health care resources. Especially, the focus is on the development of the model that can handle constraints such as managing large volume of data, enabling the automatic learning to adapt to external environmental changes and operating the system on a real-time basis. Using real data collected from local public health centers, the optimal high-risk group detection model was derived incorporating optimal parameter sets. The results of the performance test for the model using test data show that the prediction accuracy of the proposed model is two times better than the natural risk of high blood pressure.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.21-30
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• 2016

Risk management system should be able to support a decision making within a short time to analyze stream data in real time. Many analytical systems consist of CPU computation and disk based database. However, it is more problematic when existing system analyzes stream data in real time. Stream data has various production periods from 1ms to 1 hour, 1day. One sensor generates small data but tens of thousands sensors generate huge amount of data. If hundreds of thousands sensors generate 1GB data per second, CPU based system cannot analyze the data in real time. For this reason, it requires fast processing speed and scalability for analyze stream data. In this paper, we present a fast visualization technique that consists of hybrid database and GPU computation. In order to evaluate our technique, we demonstrate a visual analytics system that analyzes pipeline leak using sensor and tweet data.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.992-995
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• 1996

The major roles of Power SCADA System are continuous monitoring of electrical equipments state, real-time data processing and dispatching. Especially, SCADA system demands fast response time in heavy load condition, high reliability, fault tolerance, expansion capacity for the future. According to developing computer system technology, SCADA system is changing system configuration from centralized processing system to distributed processing system. This paper describes operational benefits, problems and improvement (which is studying in theory) in the application of Open System Architecture SCADA which has been installed since 1994, Seoul regional control center in KEPCO.