Demo Data from EDBT 2026 / reviewers in the wild / expert
Ting-Kai Hwang
dblp:26/5572
· DBLP profile ↗
6ranked-venue papers
5as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 6 · 5 first-author · 2 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.
| Theoretical computer science
1 paper |
Mathematical optimization · 91% Graph algorithms and graph theory · 9% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization
lagrangian relaxation |
0.0 | 1 | 2003 | Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabrication · IEEE Trans. Robotics Autom. 2003 |
Mathematical optimization › scheduling
production scheduling |
0.0 | 1 | 2003 | Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabrication · IEEE Trans. Robotics Autom. 2003 |
Mathematical optimization
scheduling |
0.0 | 1 | 2003 | Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabrication · IEEE Trans. Robotics Autom. 2003 |
Graph algorithms and graph theory › graph algorithms
network flow |
0.0 | 1 | 2003 | Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabrication · IEEE Trans. Robotics Autom. 2003 |
Methods — techniques the papers use, named apart from their topics
hierarchical scheduling · 0.0frank-wolfe method · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Supervised Machine Learning Based Anomaly Detection in Online Social Networks
Chi-Leng Che, Ting-Kai Hwang, Yung-Ming Li |
WorldCIST (2) | 2 |
| 2022 | Machine Learning Detection for Financial Statement Fraud
Ting-Kai Hwang, Wei-Chun Chen, Wan-Chi Chiang, Yung-Ming Li |
WorldCIST (2) | 1 |
| 2020 | Design of a Network Learning System for the Usage of Surgical Instruments
Ting-Kai Hwang, Bih-Huang Jin, Su-Chiu Wang |
WorldCIST (3) | 1 |
| 2018 | Mobile Sharing Platform Operation Model and System Dynamic Analysis: Uber and Taiwan Taxi as Examples
Ting-Kai Hwang, Bih-Huang Jin, Yung-Ming Li, Shin-Jui Lee |
WorldCIST (1) | 1 |
| 2016 | A Nearby Expert Discovering Mechanism: For Social Support
Ting-Kai Hwang, Yung-Ming Li, Bih-Huang Jin |
WorldCIST (1) | 1 |
| 2003 | Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabricationabstractThis paper describes the design of a two-level hierarchical production scheduling engine, which captures the industrial practice of mass production semiconductor fabrication factories (fabs). The two levels of the hierarchy consist of a mid-term scheduler and a short-term scheduler, and are aimed at achieving coordination between the fab-wide objectives and local shop-floor operations. The mid-term scheduler maximizes weighted production flow to reduce the fab-wide cycle time and ensure on-time delivery by properly setting daily production target volumes and reference work-in-process (WIP) levels for individual part types and stages. Mid-term scheduling results are further broken down into more detailed schedules by the short-term scheduler. In addition to the same set of operational constraints in mid-term scheduling, the short-term scheduler includes the consideration of batching effects. It maximizes weighted production flow while tracking the daily production targets and the reference WIP levels specified by mid-term scheduling. The schedulers adopt a solution methodology with three ingredients; the Lagrange relaxation approach, network flow optimization, and Frank-Wolfe method. The scheduling tool is reasonably efficient in computation. Ting-Kai Hwang, Shi-Chung Chang |
IEEE Trans. Robotics Autom. | 1 |