EDBT 2026 Demo / reviewers in the wild / expert
Yen-Yu Lu
dblp:45/9345
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2025
0009-0000-7101-7211ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 77% Performance modeling and evaluation · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
i/o workload characterization |
0.8 | 1 | 2024 | A Stable Idle Time Detection Platform for Real I/O Workloads · ACM Trans. Archit. Code Optim. 2024 |
Methods — techniques the papers use, named apart from their topics
idle time detection methods · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | A speech denoising demonstration system using multi-model deep-learning neural networks
Ching-Ta Lu, Jun-Hong Shen, Aniello Castiglione, Cheng-Han Chung, Yen-Yu Lu |
Multim. Tools Appl. | 5 |
| 2024 | A Stable Idle Time Detection Platform for Real I/O WorkloadsabstractIt is important to utilize the idle time of a workload to improve the system performance. In the article, we will explore multiple idle time detection methods to predict the idle time of the real I/O workloads. The objective is to build a stable idle time detection platform by investigating the impact of multiple representative methods to pursue a more stable prediction accuracy. The experimental results show that the prediction accuracy of the proposed platform can be stable between 60% and 80%. Yen-Yu Lu, Chin-Hsien Wu, Shih-Jen Li, Cheng-Tze Lee, Cheng-Yen Wu |
ACM Trans. Archit. Code Optim. | 1 |
| 2011 | A multicriteria group decision-making approach based on interval-valued intuitionistic fuzzy sets: A comparative perspective
Ting-Yu Chen 0002, Hsiao-Pin Wang, Yen-Yu Lu |
Expert Syst. Appl. | 3 |