Can large-scale analytics become interactive while making accuracy and latency tradeoffs explicit?
A research lineage spanning BlinkDB, Verdict, VerdictDB, and SnappyData, from bounded approximation to portable analytical systems.
The problem
Exact queries over massive data can be too slow for interactive exploration, while ad hoc approximation lacks meaningful guarantees.
The approach
Use sampling, error estimation, stochastic planning, and platform-independent execution to expose bounded tradeoffs and reusable analytics systems.
A research lineage spanning BlinkDB, Verdict, VerdictDB, and SnappyData, from bounded approximation to portable analytical systems.
Main contributions
- Research framing and system design
- Methods, implementation, and empirical evaluation
- Open research artifacts and scholarly dissemination
Publications
BlinkDB: queries with bounded errors and bounded response times on very large data
EuroSys 2013 · ACM European Conference on Computer Systems
Cite
@inproceedings{7c357390-71f0-46af-b841-6dd64ddd5193,
title = {BlinkDB: queries with bounded errors and bounded response times on very large data},
author = {Sameer Agarwal and Barzan Mozafari and Aurojit Panda and Henry Milner and Samuel Madden and Ion Stoica},
booktitle = {ACM European Conference on Computer Systems},
year = {2013},
doi = {10.1145/2465351.2465355}
}The analytical bootstrap: a new method for fast error estimation in approximate query processing
SIGMOD 2014 · ACM SIGMOD International Conference on Management of Data
Cite
@inproceedings{2120f282-9549-4542-91fb-e9d4cf1d6c2f,
title = {The analytical bootstrap: a new method for fast error estimation in approximate query processing},
author = {Kai Zeng and Shi Gao and Barzan Mozafari and Carlo Zaniolo},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2014},
doi = {10.1145/2588555.2588579}
}Verdict: A System for Stochastic Query Planning
CIDR 2015 · Conference on Innovative Data Systems Research
VerdictDB: Universalizing Approximate Query Processing
SIGMOD 2018 · ACM SIGMOD International Conference on Management of Data
Cite
@inproceedings{ed762d26-878b-453f-b272-09d069f37d64,
title = {VerdictDB: Universalizing Approximate Query Processing},
author = {Yongjoo Park and Barzan Mozafari and Joseph Sorenson and Junhao Wang},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2018},
doi = {10.1145/3183713.3196905}
}Join on Samples: A Theoretical Guide for Practitioners
PVLDB 2019 · Proceedings of the VLDB Endowment
Cite
@article{6d32861d-ba95-4e8b-91c3-bc689ec60e12,
title = {Join on Samples: A Theoretical Guide for Practitioners},
author = {Dawei Huang and Dong Young Yoon and Seth Pettie and Barzan Mozafari},
journal = {Proceedings of the VLDB Endowment},
year = {2019},
doi = {10.14778/3372716.3372726}
}SnappyData
Encyclopedia of Big Data Technologies 2019
Demonstration of VerdictDB, the Platform-Independent AQP System
SIGMOD 2018 · ACM SIGMOD International Conference on Management of Data
Cite
@inproceedings{9438c730-b302-49f6-bed8-3a77c1e7a2c2,
title = {Demonstration of VerdictDB, the Platform-Independent AQP System},
author = {Wen He and Yongjoo Park and Idris Hanafi and Jacob Yatvitskiy and Barzan Mozafari},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2018},
doi = {10.1145/3183713.3193538}
}Approximate Query Engines: Commercial Challenges and Research Opportunities
SIGMOD 2017 · ACM SIGMOD International Conference on Management of Data
Cite
@inproceedings{fc569ad9-4672-4854-9fef-d8bd5b3ec51c,
title = {Approximate Query Engines: Commercial Challenges and Research Opportunities},
author = {Barzan Mozafari},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2017},
doi = {10.1145/3035918.3056098}
}SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics
CIDR 2017 · Conference on Innovative Data Systems Research
Cite
@inproceedings{e6de4a52-07a5-47c5-8196-ace46d2a49c2,
title = {SnappyData: A Unified Cluster for Streaming, Transactions and Interactice Analytics},
author = {Barzan Mozafari and Jags Ramnarayan and Sudhir Menon and Yogesh Mahajan and Soubhik Chakraborty and Hemant Bhanawat and Kishor Bachhav},
booktitle = {Conference on Innovative Data Systems Research},
year = {2017}
}SnappyData: A Hybrid Transactional Analytical Store Built On Spark
SIGMOD 2016 · ACM SIGMOD International Conference on Management of Data
Cite
@inproceedings{cea59a03-38c4-4acb-bd2d-a41ecabe154a,
title = {SnappyData: A Hybrid Transactional Analytical Store Built On Spark},
author = {Jags Ramnarayan and Barzan Mozafari and Sumedh Wale and Sudhir Menon and Neeraj Kumar and Hemant Bhanawat and Soubhik Chakraborty and Yogesh Mahajan and Rishitesh Mishra and Kishor Bachhav},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2016},
doi = {10.1145/2882903.2899408}
}Visualization-aware sampling for very large databases
ICDE 2016 · IEEE International Conference on Data Engineering
Cite
@inproceedings{fb040b81-82bc-493d-9322-fd576b39c635,
title = {Visualization-aware sampling for very large databases},
author = {Yongjoo Park and Michael J. Cafarella and Barzan Mozafari},
booktitle = {IEEE International Conference on Data Engineering},
year = {2016},
doi = {10.1109/ICDE.2016.7498287}
}A Handbook for Building an Approximate Query Engine
IEEE Data Engineering Bulletin 2015
Knowing when you're wrong: building fast and reliable approximate query processing systems
SIGMOD 2014 · ACM SIGMOD International Conference on Management of Data
Cite
@inproceedings{40bace5f-2a47-4fe3-b90a-bd42d385640b,
title = {Knowing when you're wrong: building fast and reliable approximate query processing systems},
author = {Sameer Agarwal and Henry Milner and Ariel Kleiner and Ameet Talwalkar and Michael I. Jordan and Samuel Madden and Barzan Mozafari and Ion Stoica},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2014},
doi = {10.1145/2588555.2593667}
}