Demo Data from VLDB 2026 / reviewers in the wild / expert
Guofang Qiu
dblp:93/9705 · also Guo-Fang Qiu
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 first-authorArtificial intelligence and machine learning · 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.
| Theoretical computer science
1 paper |
Logic in computer science · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Logic in computer science › knowledge representation and reasoning › uncertainty reasoning
attribute reduction |
0.1 | 1 | 2007 | A general approach to attribute reduction in rough set theory · Sci. China Ser. F Inf. Sci. 2007 |
Logic in computer science › knowledge representation and reasoning › uncertainty reasoning
rough set theory |
0.1 | 1 | 2007 | A general approach to attribute reduction in rough set theory · Sci. China Ser. F Inf. Sci. 2007 |
Methods — techniques the papers use, named apart from their topics
rough set theory · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | A mathematical model for concept granular computing systems
Guofang Qiu, Jianmin Ma, Hong-Zhi Yang, Wen-Xiu Zhang |
Sci. China Inf. Sci. | 1 |
| 2007 | A general approach to attribute reduction in rough set theory
Wen-Xiu Zhang, Guofang Qiu, Weizhi Wu 0001 |
Sci. China Ser. F Inf. Sci. | 2 |
| 2003 | A knowledge processing method for intelligent systems based on inclusion degreeabstractAbstract: The probability reasoning method, fuzzy reasoning method, evidential reasoning method and other reasoning methods are main techniques employed in intelligent systems for processing uncertain and vague information. The concept of inclusion degree was proposed earlier and it has been proved that the methods mentioned above are examples of inclusion degrees. In this paper, we introduce type S1 and type S2 inclusion degrees, discuss the relationship between them, and further propose inclusion degrees on interval numbers, divisions, vectors and set vectors. This paper addresses an uncertainty analysis method with different inclusion degrees for intelligent systems and other systems such as fuzzy relational databases. Guofang Qiu, Huaizu Li, Wen-Xiu Zhang |
Expert Syst. J. Knowl. Eng. | 1 |