Demo Data from EDBT 2026 / reviewers in the wild / expert
Hyonik Lee
dblp:99/382
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2008
0000-0002-8410-4557ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 50% Wearable and physiological sensing · 50% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Ubiquitous computing and smart environments › context-aware computing
context monitoring |
0.1 | 1 | 2008 | SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments · MobiSys 2008 |
Embedded and real-time systems › sensor systems
energy-constrained sensing |
0.0 | 1 | 2008 | SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments · MobiSys 2008 |
Methods — techniques the papers use, named apart from their topics
sensor control · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environmentsabstractProactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context monitoring with rich sensors imposes heavy workloads on mobile devices with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency. Hyukjae Jang, Hyonik Lee, Youngki Lee 0001, Souneil Park, Taiwoo Park, Junehwa Song |
MobiSys | 4 |
| 2007 | CISS: An efficient object clustering framework for DHT-based peer-to-peer applications
Hyonik Lee, Su Myeon Kim, Junehwa Song |
Comput. Networks | 2 |