• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.312-317
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• 2005

Ubiquitous computing has had an effect to politics, economics, society, culture, education and so forth. For effective management of huge Ubiquitous networks environment, various computers which are connected to networks has to decide automatic optimum with intelligence. Currently in many areas, data mining has been used effectively to construct intelligent systems. We proposed a hybrid support vector machine for Ubiquitous data mining which realized intelligent Ubiquitous computing environment. Many data were collected by sensor networks in Ubiquitous computing environment. There are many noises in these data. The aim of proposed method was to eliminate noises from stream data according to sensor networks. In experiment, we verified the performance of our proposed method by simulation data for Ubiquitous sensor networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.159-168
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• 2002

Real-time(RT) object-oriented(OO) distributed computing is a form of RT distributed computing realized with a distributed computer system structured in the form of an object network. Several approached proposed in recent years for extending the conventional object structuring scheme to suit RT applications, are briefly reviewed. Then the approach named the Real Time Simulation Programing(RTSP) structuring scheme was formulated with the goal of instigating a quantum productivity jump in the design of distributed time triggered simulation. The RTSP scheme is intended to facilitate the pursuit of a new paradigm in designing distributed time triggered simulation which is to realize real-time computing with a common and general design style that does not alienate the main-stream computing industry and yet to allow system engineers to confidently produce certifiable distributed time triggered simulation for safety-critical applications. The RTSP structuring scheme is a syntactically simple but semantically Powerful extension of the conventional object structuring approached and as such, its support tools can be based on various well-established OO programming languages such as C+ + and on ubiquitous commercial RT operating system kernels. The Scheme enables a great reduction of the designers efforts in guaranteeing timely service capabilities of application systems.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.93-105
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• 2012

A data stream is a series of tuples which are generated in real-time, incessant, immense, and volatile manner. As new information technologies are actively emerging, stream processing methods are being needed to efficiently handle data streams. Especially, finding out an efficient evaluation for a multi-way join would make outstanding contributions toward improving the performance of a data stream management system because a join operation is one of the most resource-consuming operators for evaluating queries. In this paper, in order to evaluate efficiently a multi-way join continuous query, we propose a novel method to decrease the cost of a query by eliminating unsuccessful intermediate results. For this, we propose a matrix-based structure for monitoring data streams and estimate the number of final result tuples of the query and find out unsuccessful tuples by matrix multiplication operations. And then using these information, we process efficiently a multi-way join continuous query by filtering out the unsuccessful tuples in advance before actual evaluation of the query.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.614-618
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• 2016

As information on the internet and the data from smart devices are growing, the amount of stream data is also increasing in the real world. The stream data, which is a potentially large data, requires online learnable models and algorithms. In this paper, we propose a novel class of models: predictive convolutional neural networks to be able to perform online learning. These models are designed to deal with longer patterns as the layers become higher due to layering convolutional operations: detection and max-pooling on the time axis. As a preliminary check of the concept, we chose two-month gathered GPS data sequence as an observation sequence. On learning them with the proposed method, we compared the original sequence and the regenerated sequence from the abstract information of the models. The result shows that the models can encode long-range patterns, and can generate a raw observation sequence within a low error.

• The KIPS Transactions:PartD
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• v.13D no.6 s.109
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• pp.755-764
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• 2006

In recent, data streams are generated in various application fields such as a ubiquitous computing and a sensor network, and various algorithms are actively proposed for processing data streams efficiently. They mainly focus on the restriction of their memory usage and minimization of their processing time per data element. However, in the algorithms, if data elements of a data stream are generated in a rapid rate for a time unit, some of the data elements cannot be processed in real time. Therefore, an efficient load shedding technique is required to process data streams effcientlv. For this purpose, a load shedding technique over a data stream is proposed in this paper, which is based on the predicting technique of the frequency of data element considering its current frequency. In the proposed technique, considering the change of the data stream, its threshold for tuple alive is controlled adaptively. It can help to prevent unnecessary load shedding.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• 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.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.66-74
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• 2018

Recently, there has been active studies on hand gesture recognition to increase immersion and provide user-friendly interaction in a virtual reality environment. However, most studies require specialized sensors or equipment, or show low recognition rates. This paper proposes a hand gesture recognition method using Deep Learning technology without separate sensors or equipment other than camera to recognize static and dynamic hand gestures. First, a series of hand gesture input images are converted into high-frequency images, then each of the hand gestures RGB images and their high-frequency images is learned through the DenseNet three-dimensional Convolutional Neural Network. Experimental results on 6 static hand gestures and 9 dynamic hand gestures showed an average of 92.6% recognition rate and increased 4.6% compared to previous DenseNet. The 3D defense game was implemented to verify the results of our study, and an average speed of 30 ms of gesture recognition was found to be available as a real-time user interface for virtual reality applications.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.47-58
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• 2008

With the recent development of the ubiquitous computing technology, there are increasing interest and research in technologies such as sensors and RFID related to information recognition and location positioning in various ubiquitous fields. Especially, a standard specification was required for compatibility and interoperability in various RTLS(Real-Time Locating Systems) according to the development of RTLS to provide location and status information of moving objects using the RFID Tag. For these reasons, the ISO/IEC published the RTLS standard specification for compatibility and interoperability in RTLS. Therefore, in this paper, we designed and im plemented RTLS based on the spatial DSMS(Data Stream Management Stream) for efficiently managing and searching the incoming data stream of moving objects. The spatial DSMS is an extended system of STREAM(STanford stREam datA Manager) developed by Standford University to make various spatial operations possible. RTLS based on the spatial DSMS uses the SOAP(Simple Object Access Protocol) message between client and server for interoperability and translates client's SOAP message into CQL(Continuous Query Language) of the spatial DSMS. Finally, we proved the efficiency of RTLS based on the spatial DSMS by applying it for the staff location management service.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.85-101
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• 2011

The recommender system analyzes users' preference and predicts the users' preference to items in order to recommend various items such as book, movie and music for the users. The collaborative filtering method is used most widely in the recommender system. The method uses rating information of similar users when recommending items for the target users. Performance of the collaborative filtering-based recommendation is lowered when attacker maliciously manipulates the rating information on items. This kind of malicious act on a recommender system is called 'Recommendation Attack'. When the evaluation data that are in continuous change are analyzed in the perspective of data stream, it is possible to predict attack on the recommender system. In this paper, we will suggest the method to detect attack on the recommender system by using the stream trend of the item evaluation in the collaborative filtering-based recommender system. Since the information on item evaluation included in the evaluation data tends to change frequently according to passage of time, the measurement of changes in item evaluation in a fixed period of time can enable detection of attack on the recommender system. The method suggested in this paper is to compare the evaluation stream that is entered continuously with the normal stream trend in the test cycle for attack detection with a view to detecting the abnormal stream trend. The proposed method can enhance operability of the recommender system and re-usability of the evaluation data. The effectiveness of the method was verified in various experiments.