• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.

• Journal of KIISE:Databases
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• v.36 no.1
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• pp.50-62
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• 2009

In wireless and mobile environments, data broadcasting is recognized as an effective way for data dissemination due to its benefits to bandwidth efficiency, energy-efficiency, and scalability. In this paper, we address the problem of delayed query processing raised by tree-based index structures in wireless broadcast environments, which increases the access time of the mobile clients. We propose a novel distributed index structure and a clustering strategy for streaming XML data which enable energy and latency-efficient broadcast of XML data. We first define the DIX node structure to implement a fully distributed index structure which contains tag name, attributes, and text content of an element as well as its corresponding indices. By exploiting the index information in the DIX node stream, a mobile client can access the wireless stream in a shorter latency. We also suggest a method of clustering DIX nodes in the stream, which can further enhance the performance of query processing over the stream in the mobile clients. Through extensive performance experiments, we demonstrate that our approach is effective for wireless broadcasting of XML data and outperforms the previous methods.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.65-73
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• 2008

The telematics data management deals with queries on stream data coming from moving cars. So the stream DBMS should process the large amount of data stream in real-time. In this article, previous research projects are analyzed in the aspects of query processing. And a hybrid model is introduced where query preprocessor is used to process all types of queries in one singe system. Decreasing cost and rapidly increasing Performance of devices may guarantee the utmost parallelism of the hybrid system. As a result, various types of stream DBMS queries could be processed in a uniform and efficient way in a single system.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.4
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• pp.440-448
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• 1999

본 논문은 객체지향형 분산처리 환경 하에서 오디오나 비디오 등과 같은 실시간(real-time) 스트림(stream) 데이타를 처리하는 데 필요한 소프트웨어 기반구조를 설계하고 구현한 내용을 기술한다. 본 논문에서 제시한 DAViS(Distributed Object Framework supporting Audio/Video Streaming)는, 오디오/비디오 데이타의 처리와 관련된 여러 소프트웨어 구성요소들을 분산객체로 추상화하고, 그 객체들간의 제어정보 교환경로와 오디오/비디오 데이타 전송경로를 서로 분리하여 처리한다. 분산응용프로그램 작성자는 DAViS에서 제공하는 서비스들을 이용하여, 기존의 분산프로그래밍 환경이 제공하는 것과 동일한 수준에서 오디오/비디오 데이타에 대한 처리를 표현할 수 있다. DAViS는, 새로운 형식의 오디오/비디오 데이타를 처리하는 부분을 손쉽게 통합하고, 하부 네트워크의 전송기술이나 컴퓨터시스템 관련 기술의 진보를 신속하고 자연스럽게 수용할 수 있도록 하는 유연한 구조를 가지고 있다. Abstract This paper describes the design and implementation of software framework which supports the processing of real-time stream data like audio and video in distributed object-oriented computing environment. DAViS(Distributed Object Framework supporting Audio/Video Streaming), proposed in this paper, abstracts software components concerning the processing of audio/video data as distributed objects and separates the transmission path of data between them from that of control information. Based on DAViS, distributed applications can be written in the same abstract level as is provided by the existing distributed environment in handling audio/video data. DAViS has a flexible internal structure enough to easily incorporate new types of audio/video data and to rapidly accommodate the progress of underlying network and computer system technology with very little modifications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1146-1165
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• 2022

A huge amount of data in the form of videos and images is being produced owning to advancements in sensor technology. Use of low performance commodity hardware coupled with resource heavy image processing and analyzing approaches to infer and extract actionable insights from this data poses a bottleneck for timely decision making. Current approach of GPU assisted and cloud-based architecture video analysis techniques give significant performance gain, but its usage is constrained by financial considerations and extremely complex architecture level details. In this paper we propose a data pipeline system that uses open-source tools such as Apache Spark, Kafka and OpenCV running over commodity hardware for video stream processing and image processing in a distributed environment. Experimental results show that our proposed approach eliminates the need of GPU based hardware and cloud computing infrastructure to achieve efficient video steam processing for face detection with increased throughput, scalability and better performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.7
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• pp.1150-1156
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• 2008

This paper introduces the PC clustering of the SIMD structure for a distributed processing of on-line contingency to assess a static security of a power system. To execute on-line contingency analysis of a large-scale power system, we need to use high-speed execution device. Therefore, we constructed PC-cluster system using PC clustering method of the SIMD structure and applied to a power system, which relatively shows high quality on the high-speed execution and has a low price. SIMD(single instruction stream, multiple data stream) is a structure that processes are controlled by one signal. The PC cluster system is consisting of 8 PCs. Each PC employs the 2 GHz Pentium 4 CPU and is connected with the others through ethernet switch based fast ethernet. Also, we consider N-1 line contingency that have high potentiality of occurrence realistically. We propose the distributed process algorithm of the SIMD structure for reducing too much execution time on the on-line N-1 line contingency analysis in the large-scale power system. And we have verified a usefulness of the proposed algorithm and the constructed PC cluster system through IEEE 39 and 118 bus system.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11S
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• pp.3651-3667
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• 2000

Recently, in the distributed multimedia environments based on internet, as radical growing technologies, the most of researchers focus on both streaming technology and distributed object thchnology, Specially, the studies which are tried to integrate the streaming services on the distributed object technology have been progressing. These technologies are applied to various stream service mamgements and protocols. However, the stream service management mexlels which are being proposed by the existing researches are insufficient for suporting the QoS of stream services. Besides, the existing models have the problems that cannot support the extensibility and the reusability, when the QoS-reiatedfunctions are being developed as a sub-module which is suited on the specific-purpose application services. For solving these problems, in this paper. we suggested a QoS Integrated platform which can extend and reuse using the distributed object technologies, and guarantee the QoS of the stream services. A structure of platform we suggested consists of three components such as User Control Module(UCM), QoS Management Module(QoSM) and Stream Object. Stream Object has Send/Receive operations for transmitting the RTP packets over TCP/IP. User Control ModuleI(UCM) controls Stream Objects via the COREA service objects. QoS Management Modulel(QoSM) has the functions which maintain the QoS of stream service between the UCMs in client and server. As QoS control methexlologies, procedures of resource monitoring, negotiation, and resource adaptation are executed via the interactions among these comiXments mentioned above. For constmcting this QoS integrated platform, we first implemented the modules mentioned above independently, and then, used IDL for defining interfaces among these mexlules so that can support platform independence, interoperability and portability base on COREA. This platform is constructed using OrbixWeb 3.1c following CORBA specification on Solaris 2.5/2.7, Java language, Java, Java Media Framework API 2.0, Mini-SQL1.0.16 and multimedia equipments. As results for verifying this platform functionally, we showed executing results of each module we mentioned above, and a numerical data obtained from QoS control procedures on client and server's GUI, while stream service is executing on our platform.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.163-173
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• 2016

Recently, massive amounts of stream data have been studied for distributed processing. In this paper, we propose an incremental stream data processing method based on in-memory in big data environments. The proposed method stores input data in a temporary queue and compare them with data in a master node. If the data is in the master node, the proposed method reuses the previous processing results located in the node chosen by the master node. If there are no previous results of data in the node, the proposed method processes the data and stores the result in a separate node. We also propose a job scheduling technique considering the load and performance of a node. In order to show the superiority of the proposed method, we compare it with the existing method in terms of query processing time. Our experimental results show that our method outperforms the existing method in terms of query processing time.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Journal of KIISE
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• v.44 no.1
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• pp.84-94
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• 2017

Recently, the importance of velocity, one of the characteristics of big data (5V: Volume, Variety, Velocity, Veracity, and Value), has been emphasized in the data processing, which has led to several studies on the real-time stream processing, a technology for quick and accurate processing and analyses of big data. In this paper, we propose a Squall framework using Time-triggered Message-triggered Object (TMO) technology, a model that is widely used for processing real-time big data. Moreover, we provide a description of Squall framework and its operations under a single node. TMO is an object model that supports the non-regular real-time processing method for certain conditions as well as regular periodic processing for certain amount of time. A Squall framework can support the real-time event stream of big data and micro-batch processing with outstanding performances, as compared to Apache storm and Spark Streaming. However, additional development for processing real-time stream under multiple nodes that is common under most frameworks is needed. In conclusion, the advantages of a TMO model can overcome the drawbacks of Apache storm or Spark Streaming in the processing of real-time big data. The TMO model has potential as a useful model in real-time big data processing.