EDBT 2026 Demo / reviewers in the wild / expert
Gabriel Kahn
dblp:286/4357
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 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.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Data integration and cleaning · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data integration and cleaning › heterogeneous data integration
multimodal data integration |
0.5 | 1 | 2021 | Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News · SIGMOD Conference 2021 |
Methods — techniques the papers use, named apart from their topics
personalized newsletter generation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local NewsabstractGenerating hyper-local news at scale is challenging because publicly available data is not provided at the desired spatial and temporal granularity. Besides, there is a lack of automated analytical and publishing tools. Crosstown Foundry, which is being actively developed and used by engineers and journalists, is a novel data-driven system that leverages a massive multi-modal dataset to generate personalized newsletters for Los Angeles County readers. Luciano Nocera, George Constantinou, Luan V. Tran, Seon Ho Kim, Gabriel Kahn, Cyrus Shahabi |
SIGMOD Conference | 5 |