In April 2021, Facebook launched the Next-generation Data Infrastructure request for proposals (RFP). Today, we’re announcing the winners of this award.
VIEW RFPThe Facebook Core Data and Data Infra teams were interested in proposals that sought out innovative solutions to the challenges that still remain in the data management community. Areas of interest included, but were not limited to, the following topics:
- Large-scale query processing
- Physical layout and IO optimizations
- Data management and processing at a global scale
- Converged architectures for data wrangling, machine learning, and analytics
- Advances in testing and verification for storage and processing systems
Read our Q&A with database researchers Stavros Harizopoulos and Shrikanth Shankar to learn more about database research at Facebook, the goal of this RFP, and the inspiration behind the RFP.
The team reviewed 109 high-quality proposals, and we are pleased to announce the 10 winning proposals and six finalists. Thank you to everyone who took the time to submit a proposal, and congratulations to the winners.
Research award recipients
Holistic optimization for parallel query processing
Paraschos Koutris (University of Wisconsin–Madison)
SCALER – SCalAbLe vEctor pRocessing of SPJG-Queries
Wolfgang Lehner, Dirk Habich (Technische Universität Dresden)
AnyScale transactions in the cloud
Natacha Crooks, Joe Hellerstein (University of California, Berkeley)
Proudi: Predictability on unpredictable data infrastructure
Haryadi S. Gunawi (University of Chicago)
Making irregular partitioning practical
Spyros Blanas (The Ohio State University)
Dynamic join processing pushdown in Presto
Daniel Abadi, Chujun Song (University of Maryland, College Park)
A learned persistent key-value store
Tim Kraska (Massachusetts Institute of Technology)
Building global-scale systems using a flexible consensus substrate
Faisal Nawab (University of California, Irvine)
Runtime-optimized analytics using compilation hints
Anastasia Ailamaki (Swiss Federal Institute of Technology Lausanne)
Flexible scheduling for machine learning data processing close to storage
Ana Klimovic, Damien Aymon (ETH Zurich)
Finalists
Next generation data provenance/data governance
Tim Kraska, Michael Cafarella, Michael Stonebraker (Massachusetts Institute of Technology)
Optimizing commitment latency for geo-distributed transactions
Xiangyao Yu (University of Wisconsin–Madison)
Semantic optimization of recursive queries
Dan Suciu (University of Washington)
Towards a disaggregated database for future data centers
Jianguo Wang (Purdue University)
Unified data systems for structured and unstructured data
Matei Zaharia, Christos Kozyrakis (Stanford University)
Unifying machine learning and analytics under a single data engine
Stratos Idreos (Harvard University)
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