Demo Data from EDBT 2026 / reviewers in the wild / expert
Marie-Charlotte Dumargne
dblp:351/0730 · also Charlotte Dumargne
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 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 · 72% Cloud and datacenter computing · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
archival storage |
0.7 | 1 | 2023 | Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data Lakes · Proc. VLDB Endow. 2023 |
Cloud and datacenter computing › cloud data management
cloud data lake |
0.7 | 1 | 2023 | Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data Lakes · Proc. VLDB Endow. 2023 |
Storage systems › storage devices › molecular data storage
DNA storage |
0.7 | 1 | 2023 | Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data Lakes · Proc. VLDB Endow. 2023 |
Storage systems
digital preservation |
0.2 | 1 | 2023 | Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data Lakes · Proc. VLDB Endow. 2023 |
Storage systems
storage reliability |
0.2 | 1 | 2023 | Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data Lakes · Proc. VLDB Endow. 2023 |
Methods — techniques the papers use, named apart from their topics
synthetic DNA · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Towards Migration-Free Just-In-Case Data Archival for Future Cloud Data LakesabstractGiven the growing adoption of AI, cloud data lakes are facing the need to support cost-effective "just-in-case" data archival over long time periods to meet regulatory compliance requirements. Unfortunately, current media technologies suffer from fundamental issues that will soon, if not already, make cost-effective data archival infeasible. In this paper, we present a vision for redesigning the archival tier of cloud data lakes based on a novel, obsolescence-free storage medium-synthetic DNA. In doing so, we make two contributions: (i) we highlight the challenges in using DNA for data archival and list several open research problems, (ii) we outline OligoArchive-DSM (OA-DSM)-an end-to-end DNA storage pipeline that we are developing to demonstrate the feasibility of our vision. Eugenio Marinelli, Yiqing Yan, Virginie Magnone, Marie-Charlotte Dumargne, Pascal Barbry, Thomas Heinis, Raja Appuswamy |
Proc. VLDB Endow. | 4 |