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)
Incoming Non-self Citations Over Time
Authors
- 1. Paul Suganthan G.C. (University of Wisconsin)
- 2. Chong Sun (Uber; Walmart Labs)
- 3. Krishna Gayatri K. (University of Wisconsin)
- 4. Haojun Zhang (University of Wisconsin)
- 5. Frank Yang (LinkedIn; Walmart Labs)
- 6. Narasimhan Rampalli (Walmart Labs)
- 7. Shishir Prasad (Walmart Labs)
- 8. Esteban Arcaute (Walmart Labs)
- 9. Ganesh Krishnan (Walmart Labs)
- 10. Rohit Deep (Walmart Labs)
- 11. Vijay Raghavendra (Walmart Labs)
- 12. AnHai Doan (University of Wisconsin)
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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 319 | Declarative Information Extraction Using Datalog with Embedded Extraction Predicates | 2007 | VLDB | 0.00021377065 |
| 439 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018464913 |
| 2,219 | Chimera: Large-Scale Classification using Machine Learning, Rules, and Crowdsourcing | 2014 | VLDB | 8.9303727e-05 |
| 2,647 | Building, Maintaining, and Using Knowledge Bases: A Report from the Trenches | 2013 | SIGMOD | 8.2959636e-05 |
| 3,732 | Entity Resolution with Evolving Rules | 2010 | VLDB | 7.1655038e-05 |
| 4,547 | Mind the Gap: Large-Scale Frequent Sequence Mining | 2013 | SIGMOD | 6.6373455e-05 |
| 5,246 | Entity Extraction, Linking, Classification, and Tagging for Social Media: A Wikipedia-Based Approach | 2013 | VLDB | 6.3012556e-05 |
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