Demo Data from VLDB 2026 / reviewers in the wild / expert
Xiangrong Kong
dblp:39/1064
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Security and privacy · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1
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.
| Databases, data mining, and information retrieval
1 paper |
Data mining · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › pattern mining
association rule mining |
0.2 | 1 | 2013 | QuantMiner for mining quantitative association rules · J. Mach. Learn. Res. 2013 |
Data mining › pattern mining › association rule mining
quantitative association rules |
0.2 | 1 | 2013 | QuantMiner for mining quantitative association rules · J. Mach. Learn. Res. 2013 |
Bioinformatics and computational biology › gene expression analysis
differential expression analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Bioinformatics and computational biology
gene expression analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Bioinformatics and computational biology › gene expression analysis
microarray data analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Bioinformatics and computational biology › gene expression analysis › microarray data preprocessing
probe-level analysis |
0.1 | 1 | 2006 | SScore: an R package for detecting differential gene expression without gene expression summaries · Bioinform. 2006 |
Data mining
pattern mining |
0.0 | 1 | 2013 | QuantMiner for mining quantitative association rules · J. Mach. Learn. Res. 2013 |
Methods — techniques the papers use, named apart from their topics
s-score algorithm · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | The relationship between digital information security of the supply chain and enterprise developmentabstractThis study aims to enhance the core competitiveness of enterprises in the competitive environment and realise the rapid and sound development of enterprise security. The relationship between the digital transformation of the supply chain and the core competitiveness of enterprises is discussed from the perspective of constructing the information security (IS) of the internet of things (IoT). Firstly, the ciphertext-policy attribute-based encryption (CP-ABE) model of the information centre is established to study the technical problems of information encryption of IoT enterprises. Secondly, the correlation analysis method is used to determine the impact of supply chain transformation on the future development of enterprises through the correlation between the digital transformation of the supply chain and enterprise competitiveness. Finally, targeted solutions are proposed for the digital transformation of the supply chain and the IS of IoT of enterprises. Zhezhou Li, Xiangrong Kong, Xiaozhen Jiang |
Int. J. Inf. Comput. Secur. | 2 |
| 2013 | QuantMiner for mining quantitative association rules
Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard |
J. Mach. Learn. Res. | 4 |
| 2006 | SScore: an R package for detecting differential gene expression without gene expression summariesabstractSUMMARY: SScore is an R package that facilitates the comparison of gene expression between Affymetrix GeneChips using the S-score algorithm. The S-score algorithm uses probe level data directly to assess differences in gene expression, without requiring a preliminary separate step of probe set expression summary estimation. Therefore, the algorithm avoids introduction of error associated with the expression summary estimation process and has been demonstrated to improve the accuracy of identifying differentially expressed genes. The S-score produces accurate results even when few or no replicates are available. AVAILABILITY: The R package SScore is available from Bioconductor at http://www.bioconductor.org Richard E. Kennedy, Robnet T. Kerns, Xiangrong Kong, Kellie J. Archer, Michael F. Miles |
Bioinform. | 3 |