Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Qingqi Yue

dblp:183/9965 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
public administration
0.212016
Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program · KDD 2016
Mathematical optimization › least squares
constrained least squares
0.212016
Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program · KDD 2016
Performance modeling and evaluation
queueing models
0.112016
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
YearPublicationVenuePosition
2016 Batch Model for Batched Timestamps Data Analysis with Application to the SSA Disability Program
abstract
The 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
KDD1