• 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.

• The KIPS Transactions:PartD
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• v.17D no.5
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• pp.353-358
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• 2010

Unlike the traditional databases, queries on XML streams are restricted to a real time processing and memory usage. In this paper, a robust labeling scheme is proposed, which quickly identifies structural relationship between XML fragments. The proposed labeling scheme provides an effective query processing by removing many redundant operations and minimizing the number of fragments being processed. In experimental results, the proposed labeling scheme efficiently processes query processing and optimizes memory usage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5654-5668
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• 2018

Driven by security and real-time demands of Internet of Things (IoT), the timing of fog computing and edge computing have gradually come into place. Gateways bear more nearby computing, storage, analysis and as an intelligent broker of the whole computing lifecycle in between local devices and the remote cloud. In fog computing, the edge broker requires X-aware capabilities that combines software programmability, stream processing, hardware optimization and various connectivity to deal with such as security, data abstraction, network latency, service classification and workload allocation strategy. The prosperous of Field Programmable Gate Array (FPGA) pushes the possibility of gateway capabilities further landed. In this paper, we propose a software-defined gateway (SDG) scheme for fog computing paradigm termed as Fog Computing Zero-Knowledge Gateway that strengthens data protection and resilience merits designed for industrial internet of things or highly privacy concerned hybrid cloud scenarios. It is a proxy for fog nodes and able to integrate with existing commodity gateways. The contribution is that it converts Privacy-Enhancing Technologies rules into provable statements without knowing original sensitive data and guarantees privacy rules applied to the sensitive data before being propagated while preventing potential leakage threats. Some logical functions can be offloaded to any programmable micro-controller embedded to achieve higher computing efficiency.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.1153-1158
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• 2000

본 논문에서는 H.261로 압축된 비디오 데이터에서 움직임 검출과 장면 변환과 같은 이벤트를 압축된 정보인 움직임벡터를 이용하여 검출하는 새로운 알고리즘을 제안하고 구현하였다. 본 논문에서 제시하는 알고리즘은 압축된 데이터를 이용함으로써 성능 향상은 물론 움직임 검출, 움직임 기간 검출, I 프레임 검출 등의 다양한 이벤트의 실시간 검출을 가능하게 한다.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.318-323
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• 2005

This research evaluates agricultural water supply capabilities for water computing demand and supply for water of the whole water system of Ansung stream by carrying out basin water balance classified by irrigation facility of water system of Ansung stream.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.117-128
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• 2006

In this paper, we propose a streaming method for XML data that supports energy-efficient processing of queries over the stream in mobile clients. We propose new stream organizations for XML data which have different kinds of addresses to related data in a stream. We describe event-driven stream generation algorithms for the proposed stream structures and provide search algorithms for simple XML path queries which leverage the access mechanisms incorporated in the stream. Experimental results show that our approaches can effectively improve the tuning time performance of user queries in a wireless broadcasting environment.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.1-16
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• 2006

Recently, human based computing environment for supporting users to concentrate only user task without sensing other changes from users is being progressively researched and developed. But middleware deletes steream data processed for reducing process load of massive information from RFID sensor in this computing. So, this kind of middleware have problems when user demands probability or statistics needed for data warehousing or data mining and when user demands very important stream data repeatedly but already discarded in the middleware every former time. In this paper, we designs Sensor Middleware Architecture on Multi Level Spatial DBMS with Snapshot and manage repeatedly required stream datas to solve reusing problems of historical stream data in current middleware. This system uses disk databse that manages historical stream datas filtered in middleware for requiring services using historical stream information as data mining or data warehousing from user, and uses memory database that mamages highly reuseable data as a snapshot when stream data storaged in disk database has high reuse frequency from user. For the more, this system processes memory database management policy in a cycle to maintain high reusement and rapid service for users. Our paper system solves problems of repeated requirement of stream datas, or a policy decision service using historical stream data of current middleware. Also offers variant and rapid data services maintaining high data reusement of main memory snapshot datas.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.21-29
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• 2012

In ubiquitous computing environment, rising large scale data exchange through sensor network with sharply growing the internet, the processing of the continuous stream data is required. Therefore there are some mining researches related to the extracting of frequent structures and the efficient query processing of XML stream data. In this paper, we propose a mining method to extract frequent structures of XML stream data in recent window based on the active window sliding using trigger rule. The proposed method is a basic research to control the stream data flow for data mining and continuous query by trigger rules.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.254-265
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• 2003

This paper presents the design and implementation of stream control mechanisms which are necessary for the development of an efficient streaming cache server. The streaming protocols used in our implementation are the RTSP/RTP/RTCP standards. The mechanisms support both the on-demand media caching and real-time media splitting applications. The core of the stream control includes the session management, which handles the RTSP/RTCP control session and the RTP transport session, and the cache block management which efficiently manages the RTP-based cache blocks stored in the cache server. The streaming cache server with the proposed stream control mechanism has successfully been implemented on a Linux platform and it works well with the Apple's QTSS server and the QuickTime player for both on-demand and splitting media services.