Large-scale Predictive Analytics in Vertica: Fast Data Transfer, Distributed Model Creation, and In-database Prediction
Summary: Vertica integrates with Distributed R to cut transfers and preserve locality across table segments. In-database R model management and fast deployment enable scalable predictive analytics on large data, beating ODBC and rivaling in-memory engines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Shreya Prasad (Hewlett Packard Enterprise)
- 2. Jeff LeFevre (Hewlett Packard Enterprise)
- 3. Arash Fard (Hewlett Packard Enterprise)
- 4. Vincent Xu (Hewlett Packard Enterprise)
- 5. Vishrut Gupta (Hewlett Packard Enterprise)
- 6. Meichun Hsu (Hewlett Packard Enterprise)
- 7. Jorge Martinez (Hewlett Packard Enterprise)
- 8. Indrajit Roy (Hewlett Packard Enterprise)
BibTeX Citation
@inproceedings{prasad_sigmod15,
title = {{Large-scale Predictive Analytics in Vertica: Fast Data Transfer, Distributed Model Creation, and In-database Prediction}},
author = {Prasad, Shreya and LeFevre, Jeff and Fard, Arash and Xu, Vincent and Gupta, Vishrut and Hsu, Meichun and Martinez, Jorge and Roy, Indrajit},
series = {{SIGMOD} '15},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2723372.2742789},
url = {https://dl.acm.org/doi/10.1145/2723372.2742789},
year = {2015}
}
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,250 | Data Management in Machine Learning: Challenges, Techniques, and Systems | 2017 | SIGMOD | 0.00011485301 |
| 2,162 | Heterogeneity-aware Distributed Parameter Servers | 2017 | SIGMOD | 9.0581831e-05 |
| 2,823 | Query Processing on Tensor Computation Runtimes | 2022 | VLDB | 8.0893814e-05 |
| 6,865 | SparkR: Scaling R Programs with Spark | 2016 | SIGMOD | 5.7508365e-05 |
| 7,344 | Building the Enterprise Fabric for Big Data with Vertica and Spark Integration | 2016 | SIGMOD | 5.638371e-05 |
| 12,060 | dmapply: A functional primitive to express distributed machine learning algorithms in R | 2016 | VLDB | 5.093636e-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 |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 106 | The MADlib Analytics Library or MAD Skills, the SQL | 2012 | VLDB | 0.00033539462 |
| 186 | The Vertica Analytic Database: C-Store 7 Years Later | 2012 | VLDB | 0.00026182534 |
| 372 | HaLoop: Efficient Iterative Data Processing on Large Clusters | 2010 | VLDB | 0.0001981521 |
| 1,333 | Ricardo: Integrating R and Hadoop | 2010 | SIGMOD | 0.0001112858 |
| 1,742 | ArrayStore: A Storage Manager for Complex Parallel Array Processing | 2011 | SIGMOD | 9.8748669e-05 |
| 5,855 | Bridging Two Worlds with RICE: Integrating R into the SAP In-Memory Computing Engine | 2011 | VLDB | 6.064726e-05 |
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|---|---|---|---|---|
| 1 | 8,721 | Accelerate Distributed Joins with Predicate Transfer | 2025 | SIGMOD |
| 2 | 7,959 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB |
| 3 | 9,640 | Supporting Scalable Analytics with Latency Constraints | 2015 | VLDB |
| 4 | 11,885 | Integration of Large-Scale Data Processing Systems and Traditional Parallel Database Technology | 2019 | VLDB |
| 5 | 4,067 | Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches | 2021 | VLDB |
| 6 | 518 | Towards a Unified Architecture for in-RDBMS Analytics | 2012 | SIGMOD |
| 7 | 186 | The Vertica Analytic Database: C-Store 7 Years Later | 2012 | VLDB |
| 8 | 12,060 | dmapply: A functional primitive to express distributed machine learning algorithms in R | 2016 | VLDB |
| 9 | 7,344 | Building the Enterprise Fabric for Big Data with Vertica and Spark Integration | 2016 | SIGMOD |
| 10 | 2,347 | Vertica-ML: Distributed Machine Learning in Vertica Database | 2020 | SIGMOD |