• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.177-180
• /
• 2010

RFID를 기반으로 유비쿼터스 환경의 응용 서비스를 지원하는 미들웨어는 지속적으로 끊임없이 입력되는 데이터 스트림을 실시간으로 처리하고 응용 서비스에서 요구하는 결과를 획득하여 전달해야 한다. 이와 같은 요구사항을 만족하기 위해 데이터 스트림 관리 시스템(DSMS)이 제안되었으며 다양한 연구가 시도되고 있다. 본 논문에서는 대량의 이벤트가 입력되는 환경에서 우선순위가 높은 질의를 실시간으로 처리하기 위한 DSMS를 제안하고 있다. 본 연구는 스탠포드의 STREAM 프로젝트를 활용하여 설계 및 구현하였으며, 각 쿼리를 State Thread로 동작시키는 방법을 이용하였다. 쓰레드 라이브러리의 스케줄러 부분을 실시간 스케줄러로 개선하는 작업을 진행하였으며, 실험을 통하여 쓰레드 스케줄러가 질의에 대해서 실시간으로 스케줄링을 할 수 있음을 보이고 있다.

• Electronics and Telecommunications Trends
• /
• v.19 no.5 s.89
• /
• pp.21-30
• /
• 2004

마크와이저에 의하여 제창된 유비쿼터스 컴퓨팅을 가능하게 하는 차세대 핵심 기술로 RFID 기술이 주목받고 있으며, RFID를 기반으로 하는 유비쿼터스 컴퓨팅 환경에서 응용 서비스를 제공하기 위하여 다양한각도에서 연구 개발이 진행되고 있다. 본 고에서는 RFID를 기반으로 하는 유비쿼터스 컴퓨팅 환경을 구현하기 위한 EPCglobal의 EPC Network Architecture의 구성 요소에 대하여 알아보고 이를 지원하는 미들웨어 제품 및 솔루션을 살펴보고, 연속적으로 들어오는 데이터 스트림을 실시간으로 처리하기 위해 수행되고 있는 프로젝트들의 기술 동향에 대하여 논의한다.

• The Journal of Society for e-Business Studies
• /
• v.14 no.4
• /
• pp.75-94
• /
• 2009

Since XML emerged as a standard for data exchange on the web, it has been established as a core component in e-Commerce and efficient query processing over XML data in ubiquitous computing environment has been also receiving much attention. Recently, the techniques were proposed whereby an XML document is fragmented into XML fragments to be streamed and the mobile clients receive the stream while processing queries over it. In processing queries over an XML fragment stream, the average access time significantly depends on the order of fragments in the stream. As such, for query performance, an efficient organization of XML fragment stream is required as well as the indexing for energy-efficient query processing due to the reduction of tuning time. In this paper, a technique of XML fragment stream organization based on query frequencies, fragment size, fragment access frequencies, and an active XML-based indexing scheme are proposed. Through implementation and performance experiments, our techniques were shown to be efficient compared with the conventional XML fragment stream organizations.

• KIISE Transactions on Computing Practices
• /
• v.21 no.8
• /
• pp.513-523
• /
• 2015

A complex event processing system, becoming useful in real life domains, efficiently processes stream of continuous events like sensor data from IoT systems. However, those systems do not work well on some types of queries yet, so that programmers should be careful about that. For instance, they do not sufficiently provide detailed guide to choose efficient queries among the almost same meaning queries. In this paper, we propose an query preprocessing tool for event stream processing systems, which helps programmers by giving them the hints to improve performance whenever their queries fall in any possible bad formats in the performance sense. We expect that our proposed module would be a big help to increases productivity of writing programs where debugging, testing, and performance tuning are not straightforward.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.840-845
• /
• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.11a
• /
• pp.1179-1180
• /
• 2011

유비쿼터스 컴퓨팅 환경에서 무한의 연속적으로 전송되는 데이터에 대한 처리가 요구되고 있다. 본 논문에서는 연속적이고 빠르게 발생하는 스트림 데이터로부터 유용한 정보를 발견하기 위한 기반 연구로써 트리거를 이용한 슬라이딩 윈도우 기반의 XML 빈발 구조 추출 방법을 제안한다.

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

• 한국HCI학회:학술대회논문집
• /
• 2007.02a
• /
• pp.790-795
• /
• 2007

최근 유비쿼터스 컴퓨팅에 대한 연구가 활발히 진행되고 있으며 유비쿼터스 컴퓨팅의 실현을 위한 핵심기술로서 RFID 시스템에 대한 연구가 활발히 진행되고 있다. 유비쿼터스 환경에서 RFID 시스템이 사용자의 편리함을 가져다 주는 장점이 있는 반면, 이로 인해 사용자의 프라이버시가 침해 당할 수 있는 문제점 또한 가지고 있다. 본 논문에서 사용자 인증 알고리즘은 새로운 해쉬 함수를 사용하고 그리고 메시지 암호화를 위한 스트림 암호기는 LFSR(Linear Feedback Shift Register)을 사용한다.

• Journal of KIISE:Databases
• /
• v.35 no.1
• /
• pp.67-83
• /
• 2008

In order to realize ubiquitous computing, it is essential to efficiently use the resources and the computing power of mobile devices. Among others, memory efficiency, energy efficiency, and processing efficiency are required in executing the softwares embedded in mobile devices. In this paper, query processing over XML data in a mobile device where resources are limited is addressed. In a device with limited amount of memory, the techniques of XML. stream query processing need to be employed to process queries over a large volume of XML data Recently, a technique Galled XFrag was proposed whereby XML data is fragmented with the hole-filler model and streamed in fragments for processing. With XFrag, query processing is possible in the mobile device with limited memory without reconstructing the XML data out of its fragment stream. With the hole-filler model, however, memory efficiency is not high because the additional information on holes and fillers needs to be stored. In this paper, we propose a new technique called XFLab whereby XML data is fragmented with the XML labeling scheme which is for representing the structural relationship in XML data, and streamed in fragments for processing. Through implementation and experiments, XML showed that our XFLab outperformed XFrag both in memory usage and processing time.

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