• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2003.11a
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• pp.101-104
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• 2003

본 논문에서는 디지털TV 데이터방송에 있어 전자상거래용 연통형 데이터방송 컨텐츠를 제작학 수 있는 저작도구를 제안한다. 연통형 데이터방송이란 오디오/비디오 스트림과 데이터 스트림으로 이루어진 컨텐츠로 오디오/비디오와 연동되어 애플리케이션이 실행된다. 본 논문에서 제안하는 저작 시스템은 비디오와 상품정보인 부가데이터의 동기화 작업을 수행하여, 비디오의 특정시간에 상품을 광고하고 구매를 할 수 있는 효과적인 전자상거래 컨텐츠를 제작 학 수 있도록 하여 준다. 애플리케이션 제작에서부터 전송스트림 생성까지 통합적인 데이터방송 컨텐츠 제작환경을 제공함으로서 연통형 데이터방송 실시에 대비하여 매우 적합한 도구이며, 사용자의 편리성과 효율성을 위하여 손쉬운 사용자 인터페이스를 제공하여 활용을 용이하게 하였다.

• 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.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.221-224
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• 2001

네트워크 기술의 발전으로, 인터넷을 통한 주문형 비디오 서비스가 가능해지고 있다. 주문형 비디오 시스템은 대용량 데이터를 실시간에 전송할 필요가 있기 때문에 디스크 전송대역폭은 매우 중요한 자원이다. 동시 다수에게 끊긴 현상이 없는 원활한 스트림 서비스를 하기 위해서는 매우 큰 디스크 대역폭이 필요하다. 비디오 스트림 데이터를 캐슁하여 디스크 대역폭을 절약하면, 한정된 디스크 대역폭에서 더 많은 스트림을 제공할 수 있다. 논문은 보다 향상된 비디오 스트림 캐슁 알고리즘을 제안한다. 제안된 알고리즘은 기존의 방법들 보다 더 많은 스트림을 제공하고, 스트림 재생의 지연을 줄인다.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.1-9
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• 2017

Smart Cities intelligently manage numerous infrastructures, including Smart-City IoT devices, and provide a variety of smart-city applications to citizen. In order to provide various information needed for smart-city applications, Smart Cities require a function to intelligently process large-scale streamed big data that are constantly generated from a large number of IoT devices. To provide smart services in Smart-City, the Smart-City Consortium uses stream reasoning. Our stream reasoning requires real-time processing of big data. However, there are limitations associated with real-time processing of large-scale streamed big data in Smart Cities. In this paper, we introduce one of our researches on cloud computing based real-time distributed-parallel-processing to be used in stream-reasoning of IoT big data in Smart Cities. The Smart-City Consortium introduced its previously developed smart-city middleware. In the research for this paper, we made cloud computing based real-time distributed-parallel-processing available in the cloud computing platform of the smart-city middleware developed in the previous research, so that we can perform real-time distributed-parallel-processing with them. This paper introduces a real-time distributed-parallel-processing method and system for stream reasoning with IoT big data transmitted from various sensors of Smart Cities and evaluate the performance of real-time distributed-parallel-processing of the system where the method is implemented.

• Journal of Korea Multimedia Society
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• v.15 no.1
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• pp.87-92
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• 2012

Wireless sensor network is a technology that collects the information about object in real-time. Sensor data has a characteristic that is generated an unprecedented volume data in short time. Analysis is essential to define the relationship between the data, including more of the data from a large volume data stream which is acquired from the sensor. In order to effectively handle the sensor data stream, in this paper, using ECA rules to organize data in a meaningful and more practical real-time monitoring systems is proposed.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06c
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• pp.43-45
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• 2006

최근에 데이터의 양이 방대하게 늘어남에 따라 이러한 데이터의 처리를 위한 여러 연구들이 진행되어지고 있다. 이 중에 데이터들 간의 군집 관계를 파악하기 위하여 사용되는 클러스터링에 관한 연구가 많이 수행되었는데, 이중 BIRCH는 대용량의 데이터를 처리하는데 있어서 적합한 모델로 제시되고 있다. 하지만 BIRCH는 데이터 스트림 환경에서 클러스터링은 효과적이지 못한 단점을 가지고 있다. 본 논문은 데이터 스트림 환경에서 과거의 임의 시간구간에 대한 클러스터링을 수행하는 방법을 제안한다. 이를 위하여 CF-트리를 일정 시간 마다 생성 및 저장하고 이를 이용하여 사용자가 원하는 시간 구간에 대해 동안의 클러스터링을 수행한다. 본 논문에서는 임의 시간구간에 대한 효율적인 클러스터링을 위해 기존의 CF-트리 노드 구조에 추가 데이터를 사용하는 $CF^{\delta}$-트리를 제안한다. 그리고 ${\delta}$에 대한 연구를 통해, 근사적 접근법을 제안하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.2
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• pp.103-110
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• 2011

It is necessary to accurate and efficient management for measured digital data from sensors in u-health system. It is not efficient that sensor network process input stream data of mass storage stored in database the same time. We propose to improve the processing performance of multidimensional stream data continuous incoming from multiple sensor. We propose process query based on sliding window for efficient input stream and found multiple query plan to Mjoin method and we reduce stored data using backpropagation algorithm. As a result, we obtained to efficient result about 18.3% reduction rate of database using 14,324 data sets.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.1-9
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• 2015

In most techniques of information differentiating for data stream mining, they give larger weight to the information generated in recent compared to the old information. However, there can be important one among the old information. For example, in case of a person was a regular customer in a retail store but has not come to the store in recent, old information with the shopping record of the person can be importantly used in a target marketing for increasing sales. In this paper, highly attention itemsets(HAI) are defined, which mean the itemsets generated in the past frequently but not generated in recent. In addition, a twao-way decay mechanism and a data stream mining method for finding HAI are proposed.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.11-20
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• 2012

Stream data shows a sequence of values changing continuously over time. Due to the nature of stream data, its trend is continuously changing according to various time intervals. Therefore the prediction of stream data must be carried out simultaneously with respect to multiple intervals, i.e. Continuous Multiple Prediction(CMP). In this paper, we propose a Continuous Integrated Hierarchical Temporal Memory (CIHTM) network for CMP based on the Hierarchical Temporal Memory (HTM) model which is a neocortex leraning algorithm. To develop the CIHTM network, we created three kinds of new modules: Shift Vector Senor, Spatio-Temporal Classifier and Multiple Integrator. And also we developed learning and inferencing algorithm of CIHTM network.