Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhaohua Ji
dblp:62/10145
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Distributed systems · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › phylogenetics
phylogenetic inference |
0.3 | 1 | 2017 | CloudPhylo: a fast and scalable tool for phylogeny reconstruction · Bioinform. 2017 |
Bioinformatics and computational biology › phylogenetics
phylogeny reconstruction |
0.3 | 1 | 2017 | CloudPhylo: a fast and scalable tool for phylogeny reconstruction · Bioinform. 2017 |
Distributed systems
distributed data processing |
0.1 | 1 | 2017 | CloudPhylo: a fast and scalable tool for phylogeny reconstruction · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
spark · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | CloudPhylo: a fast and scalable tool for phylogeny reconstructionabstractSummary: Phylogeny reconstruction is fundamentally crucial for molecular evolutionary studies but remains computationally challenging. Here we present CloudPhylo, a tool built on Spark that is capable of processing large-scale datasets for phylogeny reconstruction. As testified on empirical data, CloudPhylo is well suited for big data analysis, achieving high efficiency and good scalability on phylogenetic tree inference. Availability and Implementation: https://github.com/XingjianXu/cloudphylo Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online. Xingjian Xu, Zhaohua Ji, Zhang Zhang 0002 |
Bioinform. | 2 |