Demo Data from EDBT 2026 / reviewers in the wild / expert
Yosef Shatsky
dblp:13/6898
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2Databases, data management, data science and information retrieval · 1
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
2 papers |
Storage systems · 71% Cloud and datacenter computing · 29% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems › data reduction
data deduplication |
0.8 | 2 | 2020 | Sketching Volume Capacities in Deduplicated Storage · ACM Trans. Storage 2020 Sketching Volume Capacities in Deduplicated Storage · FAST 2019 |
Cloud and datacenter computing › resource prediction
capacity estimation |
0.4 | 1 | 2020 | Sketching Volume Capacities in Deduplicated Storage · ACM Trans. Storage 2020 |
Storage systems › flash and SSD
SSD array |
0.1 | 1 | 2020 | Sketching Volume Capacities in Deduplicated Storage · ACM Trans. Storage 2020 |
Storage systems
storage reliability |
0.1 | 1 | 2019 | Sketching Volume Capacities in Deduplicated Storage · FAST 2019 |
Methods — techniques the papers use, named apart from their topics
sketching · 0.8analytical accuracy guarantees · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Sketching Volume Capacities in Deduplicated StorageabstractThe adoption of deduplication in storage systems has introduced significant new challenges for storage management. Specifically, the physical capacities associated with volumes are no longer readily available. In this work, we introduce a new approach to analyzing capacities in deduplicated storage environments. We provide sketch-based estimations of fundamental capacity measures required for managing a storage system: How much physical space would be reclaimed if a volume or group of volumes were to be removed from a system (the reclaimable capacity) and how much of the physical space should be attributed to each of the volumes in the system (the attributed capacity). Our methods also support capacity queries for volume groups across multiple storage systems, e.g., how much capacity would a volume group consume after being migrated to another storage system? We provide analytical accuracy guarantees for our estimations as well as empirical evaluations. Our technology is integrated into a prominent all-flash storage array and exhibits high performance even for very large systems. We also demonstrate how this method opens the door for performing placement decisions at the data-center level and obtaining insights on deduplication in the field. Danny Harnik, Moshe Hershcovitch, Yosef Shatsky, Amir Epstein, Ronen I. Kat |
ACM Trans. Storage | 3 |
| 2019 | Sketching Volume Capacities in Deduplicated Storage
Danny Harnik, Moshe Hershcovitch, Yosef Shatsky, Amir Epstein, Ronen I. Kat |
FAST | 3 |