Demo Data from VLDB 2026 / reviewers in the wild / expert
Amine Ferdjaoui
dblp:339/4791
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
0009-0008-3284-030XORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Databases, data management, data science and information retrieval · 2 · 2 first-author · 2 since 2021
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
2 papers |
Information retrieval · 58% Data mining · 42% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval
text analysis |
1.6 | 2 | 2025 | Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration · WSDM 2025 WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024 |
Data mining
clustering |
0.9 | 1 | 2025 | Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration · WSDM 2025 |
Data mining › clustering
weighted clustering |
0.9 | 1 | 2025 | Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration · WSDM 2025 |
Knowledge, reasoning and agents › Knowledge representation and reasoning › causal reasoning
causal graphical model |
0.8 | 1 | 2024 | WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024 |
Information retrieval › interactive information retrieval › exploratory search
topic exploration |
0.8 | 1 | 2024 | WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024 |
Visualization and visual analytics
interactive visualization |
0.2 | 1 | 2024 | WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text Data · WSDM 2024 |
Methods — techniques the papers use, named apart from their topics
causal graphical model · 1.5weighted clustering · 0.9causal graphical models · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Cluster Insight: A Weighted Clustering Tool for Large Textual Data ExplorationabstractInternational audience Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif |
WSDM | 1 |
| 2024 | WordGraph: A Python Package for Reconstructing Interactive Causal Graphical Models from Text DataabstractWe present WordGraph, a Python package for exploring the topics of documents corpora. WordGraph provides causal graphical models from text data vocabulary and proposes interactive visualizations of terms networks. Our ease-to-use package is provided with a pre-built pipeline to access the main modules through jupyter widgets. It results in the encapsulation of a whole vocabulary exploration process within a single jupyter notebook cell, with straightforward parameters settings and interactive plots. WordGraph pipeline is fully customizable by adding/removing widgets or changing default parameters. To assist users with no background in Python nor jupyter notebook, but willing to explore large corpora topics, we also propose an automatic dashboard generation from the customizable jupyter notebook pipeline in a web application style. WordGraph is available through a GitHub repository at https://github.com/MLDS-software/WordGraph. Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif |
WSDM | 1 |