Gabriel Kahn

dblp:286/4357 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Data integration and cleaning › heterogeneous data integration
multimodal data integration
0.512021
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
YearPublicationVenuePosition
2021 Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News
abstract
Generating 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 Conference5