SummaryStore is an approximate time–series store, designed for analytics, capable of storing large volumes of time-series data (∼1 petabyte) on a single node; it preserves high degrees of query accuracy and enables near real-time querying at unprecedented cost savings. SummaryStore contributes time-decayed summaries, a novel abstraction for summarizing data streams, and returns reliable error estimates alongside the approximate answers, supporting a range of machine learning and analytical workloads.



SummaryStore to appear at SOSP '17.
Position paper on the role of approximate storage systems in machine learning to appear at AISys, co-located with SOSP '17.
Open-sourcing underway. Please check back later for updates.

Team Members

Nitin Agrawal (contact)
Ashish Vulimiri


Low-Latency Analytics on Colossal Data Streams with SummaryStore
Nitin Agrawal, Ashish Vulimiri.
Proceedings of the 26th Symposium on Operating Systems Principles (SOSP '17), Shanghai, China, October, 2017.
Learning with Less: Can Approximate Storage Systems Save Learning From Drowning in Data?
Nitin Agrawal, Ashish Vulimiri
Workshop on AI Systems at Symposium on Operating Systems Principles (SOSP), Shanghai, China, October 28, 2017.