EDBT 2026 Demo / reviewers in the wild / expert
Minjeong Yuk
dblp:346/2489
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0000-0002-4328-7078ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 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 · 93% Memory systems · 7% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
flash and SSD |
0.7 | 1 | 2023 | All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023 |
Storage systems › key-value storage
flash-based key-value caching |
0.7 | 1 | 2023 | All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023 |
Storage systems
key-value storage |
0.7 | 1 | 2023 | All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023 |
Storage systems › flash and SSD
SSD array |
0.7 | 1 | 2023 | All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023 |
Memory systems
cache |
0.2 | 1 | 2023 | All-Flash Array Key-Value Cache for Large Objects · EuroSys 2023 |
Methods — techniques the papers use, named apart from their topics
collision-tolerant hashing · 0.7approximate metadata · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | All-Flash Array Key-Value Cache for Large ObjectsabstractWe present BigKV, a key-value cache specifically designed for caching large objects in an all-flash array (AFA). The design of BigKV is centered around the unique property of a cache: since it contains a copy of the data, exact bookkeeping of what is in the cache is not critical for correctness. By ignoring hash collisions, approximating metadata information, and allowing data loss from failures, BigKV significantly increases the cache hit ratio and keeps more useful objects in the system. Experiments on a real AFA show that our design increases the throughput by 3.1× on average and reduces the average and tail latency by 57% and 81%, respectively. Jinhyung Koo, Jinwook Bae, Minjeong Yuk, Seonggyun Oh, Jungwoo Kim 0004, Jung-Soo Park, Bryan S. Kim, Sungjin Lee 0001 |
EuroSys | 3 |