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Why Big Data Industrial Systems Need Rules and What We Can Do About It

Summary: Examines handcrafted rules in big-data systems for classification and entity matching, vs academic models. Suggests a research agenda for rule generation, evaluation, execution, and maintenance; calls for scalable rule management with learning and crowdsourcing. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5071
Venue
SIGMOD
Year
2015
Pagerank
5.9887211e-05
Overall Rank
6,068 | 58.37%
DOI
10.1145/2723372.2724784

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gc_sigmod15,
        title = {{Why Big Data Industrial Systems Need Rules and What We Can Do About It}},
        author = {G.C., Paul Suganthan and Sun, Chong and K., Krishna Gayatri and Zhang, Haojun and Yang, Frank and Rampalli, Narasimhan and Prasad, Shishir and Arcaute, Esteban and Krishnan, Ganesh and Deep, Rohit and Raghavendra, Vijay and Doan, AnHai},
        series = {{SIGMOD} '15},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2723372.2724784},
        url = {https://dl.acm.org/doi/10.1145/2723372.2724784},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
1,465 Synthesizing Entity Matching Rules by Examples 2018 VLDB 0.00010689571
4,757 Pattern Functional Dependencies for Data Cleaning 2020 VLDB 6.5220005e-05
7,083 Certus: An Effective Entity Resolution Approach with Graph Differential Dependencies (GDDs) 2019 VLDB 5.7075402e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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