Demo Data from VLDB 2026 / reviewers in the wild / expert
Yongju Song
dblp:220/8640
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 75% Memory systems · 25% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
key-value storage |
0.7 | 1 | 2023 | Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices · ASPLOS (2) 2023 |
Storage systems › storage performance
latency-bandwidth tradeoff |
0.7 | 1 | 2023 | Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices · ASPLOS (2) 2023 |
Memory systems
non-volatile memory |
0.7 | 1 | 2023 | Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices · ASPLOS (2) 2023 |
Storage systems › flash and SSD
solid-state drive |
0.7 | 1 | 2023 | Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices · ASPLOS (2) 2023 |
Methods — techniques the papers use, named apart from their topics
cross-storage concurrency control · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage DevicesabstractAs data generation has been on an upward trend, storing vast volumes of data cost-effectively as well as efficiently accessing them is paramount. At the same time, today's storage landscape continues to diversify, from high-bandwidth storage devices such as NVMe SSDs to low-latency non-volatile memory (e.g., Intel Optane DCPMM). These heterogeneous storage devices have the potential to deliver high performance in terms of bandwidth and latency with cost efficiency, while achieving the performance and cost targets together still remains a challenging problem. We provide our solution, Prism, a novel key-value store that utilizes modern heterogeneous storage devices. Prism uses heterogeneous storage devices synergistically to harness the advantages of each storage device while suppressing their downsides. We devise new techniques to balance the latency-bandwidth tradeoff when reading from SSD. For ensuring multicore scalability and crash consistency of data across heterogeneous storage media, Prism proposes cross-storage concurrency control and cross-storage crash consistency protocols. Our evaluation shows that Prism outperforms state-of-the-art key-value stores by up to 13.1× with significantly lower tail latency. Yongju Song, Wook-Hee Kim, Sumit K. Monga, Changwoo Min, Young Ik Eom |
ASPLOS (2) | 1 |