Dongman Lee
Korea Advanced Institute of Science and Technology · Computer Science
About the Lab
Professor Dongman Lee's research lab specializes in distributed systems, with a focus on scalable and efficient architectures for large-scale interactive applications. The lab investigates key challenges in distributed virtual environments (DVEs), including concurrency control, interest management, communication scalability, and group-aware middleware. Their work emphasizes real-time interaction, proactive resource management, and usability evaluation in context-aware systems, particularly for elderly users. The lab integrates advanced prediction mechanisms and entity-centric communication to enhance performance and responsiveness in dynamic, large-scale environments.
Research Overview
Research Output Trend
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Selected Papers
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.
This paper proposes a prediction based concurrency control scheme which satisfies the needs on interactive performance in large scale distributed interactive applications. The prediction based concurrency control allows users real time interactions like the optimistic approaches, while it requires no repair like the pessimistic approaches. We exploit an entity-centric multicast, where only the users surrounding a target entity multicast the requests for ownership. Ownership prediction of an enti
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
Video analytics edge computing exploiting IoT cameras has gained high attention. Running such tasks on the network edge is very challenging since video and image processing are bandwidth-hungry and computationally intensive. IoT cameras are heavily dependent on environmental factors such as the brightness of the view. In this paper, we propose an edge IoT camera virtualization architecture that enables an IoT camera to accommodate multiple application operation semantics and dynamically adjust i
Research Areas
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