DBScholar

Back to papers

Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News

Summary: Scalable data-driven journalism platform leveraging a massive multi-modal dataset to generate hyper-local newsletters. Automates analytics and personalized publishing for LA County, advancing data integration, synthesis, and automated output in newsroom workflows. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6095
Venue
SIGMOD
Year
2021
Pagerank
-
Overall Rank
13,440 | 7.79%
DOI
10.1145/3448016.3452751

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{nocera_sigmod21,
        title = {{Crosstown Foundry: A Scalable Data-driven Journalism Platform for Hyper-local News}},
        author = {Nocera, Luciano and Constantinou, George and Tran, Luan V. and Kim, Seon Ho and Kahn, Gabriel and Shahabi, Cyrus},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452751},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452751},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,768 DeepTRANS: A Deep Learning System for Public Bus Travel Time Estimation using Traffic Forecasting 2020 VLDB 7.1406582e-05
Previous Page 1 / 1 Next

Semantically Similar Papers