12 citations · 13 across the 4 of their papers we have counts for
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cs.DB2020
Towards a General Framework for ML-based Self-tuning Databases
Thomas Schmied, Diego Didona, Andreas Döring +2
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimizatio…
cs.DB2020★ 1 cited
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs
Diego Didona, Nikolas Ioannou, Radu Stoica +1
Solid-state drives (SSDs) are extensively used to deploy persistent data stores, as they provide low latency random access, high write throughput, high data density, and low cost.…
cs.DB2018
Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores
Diego Didona, Willy Zwaenepoel
This paper introduces the concept of size-aware sharding to improve tail latencies for in-memory key-value stores, and describes its implementation in the Minos key-value store. Ta…