EDBT 2026 Demo / reviewers in the wild / expert
Qingqi Yue
dblp:183/9965
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author
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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational social science and digital humanities
public administration |
0.2 | 1 | 2016 | Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program · KDD 2016 |
Mathematical optimization › least squares
constrained least squares |
0.2 | 1 | 2016 | Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program · KDD 2016 |
Performance modeling and evaluation
queueing models |
0.1 | 1 | 2016 | Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program · KDD 2016 |
Methods — techniques the papers use, named apart from their topics
batch search algorithm · 0.8constrained least squares · 0.5constrained least-squares · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability ProgramabstractThe Office of Disability Adjudication and Review (ODAR) is responsible for holding hearings, issuing decisions, and reviewing appeals as part of the Social Security Administration's disability determining process. In order to control and process cases, the ODAR has established a Case Processing and Management System (CPMS) to record management information since December 2003. The CPMS provides a detailed case status history for each case. Due to the large number of appeal requests and limited resources, the number of pending claims at ODAR was over one million cases by March 31, 2015. Our National Institutes of Health (NIH) team collaborated with SSA and developed a Case Status Change Model (CSCM) project to meet the ODAR's urgent need of reducing backlogs and improve hearings and appeals process. One of the key issues in our CSCM project is to estimate the expected service time and its variation for each case status code. The challenge is that the systems recorded job departure times may not be the true job finished times. As the CPMS timestamps data of case status codes showed apparent batch patterns, we proposed a batch model and applied the constrained least squares method to estimate the mean service times and the variances. We also proposed a batch search algorithm to determine the optimal batch partition, as no batch partition was given in the real data. Simulation studies were conducted to evaluate the performance of the proposed methods. Finally, we applied the method to analyze a real CPMS data from ODAR/SSA. Qingqi Yue, Ao Yuan, Xuan Che, Minh Huynh, Chunxiao Zhou |
KDD | 1 |