이동만 교수
Dongman Lee
KAIST 전산학부 · 컴퓨터과학
연구실 소개
이동만 교수 연구실은 분산 가상 환경(DVE)과 실시간 상호작용을 위한 스케일러블한 소프트웨어 아키텍처 설계에 중점을 두고 있습니다. 특히 통신 아키텍처, 관심 관리, 동시성 제어, 데이터 복제 등의 핵심 기술을 기반으로 대규모 사용자 환경에서의 성능과 확장성을 확보하는 데 연구를 집중하고 있습니다. 또한, 예측 기반 동시성 제어 기법을 통해 사용자 간 실시간 상호작용의 지연을 최소화하고, 그 결과로 높은 성능을 구현하는 기술적 접근을 개발하고 있습니다. 특히 엔티티 중심 멀티캐스트 기반의 소유권 예측 메커니즘은 대규모 분산 환경에서의 효율성을 극대화합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15A distributed virtual environment (DVE) is a software system that allows users in a network to interact with each other by sharing a common view of their states. As users are geographically distributed over large networks like the internet and the number of users increases, scalability is a key aspect to consider for real-time interaction. Various solutions have been proposed to improve the scalability in DVE systems but they are either focused on only specific aspects or customized to a target
A distributed virtual environment (DVE) is a software system that allows users on a network to interact with each other by sharing a common view of their states. As users are geographically distributed over large networks like the Internet and the number of users increases, scalability is a key aspect to consider for real-time interaction. Various solutions have been proposed to improve the scalability in DVE systems but they are either focused on only specific aspects or customized to a target
The aim of this research is to develop a quantitative usability evaluation method (UEM) for elderly drivers, which has different weight values on each factor concerning physical and cognitive context of elderly drivers. An analysis of the relationship between universal design guidelines for elderly drivers and usability principles was conducted by using the quality function deployment method. In addition, developmental priorities are derived from analysis results of difficulty in achieving perfo
We present active surroundings, a group-aware middleware infrastructure for embedded application systems where entities (devices or services) actively respond to user actions and help users to perform their jobs with no or minimal involvement of users. Our system focuses on two key issues: transparent application reconfiguration and group-context awareness.
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We propose an enhanced prediction-based concurrency control scheme that supports the scalability of concurrency control for large distributed virtual environments especially where entities are highly populated and tend to gather closely. The prediction scheme is based on an entity-centric multicast group. Only the users surrounding a target entity multicast the ownership requests via an entity multicast group and become owner candidates. The current owner predicts the next owner among the owner
Abstract With the expansion of the internet and its bandwidth, distributed virtual environment (DVE) applications have become more prevalent. In DVE applications, users frequently crowd in a specific place, and a key aspect to consider is how to provide interactive performance for users. However, existing approaches using multicast require users to receive uninteresting messages. Even though recent works have addressed fine‐grained filtering, they still incur other drawbacks in terms of assignin
As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private
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