Demo Data from EDBT 2026 / reviewers in the wild / expert
Geoffrey Kao
dblp:247/8421
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, 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
1 paper |
Cloud and datacenter computing · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing › cluster resource management and scheduling
cluster provisioning |
0.1 | 1 | 2019 | NashDB: Fragmentation, Replication, and Provisioning using Economic Methods · Proc. VLDB Endow. 2019 |
Methods — techniques the papers use, named apart from their topics
economic model · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | NashDB: Fragmentation, Replication, and Provisioning using Economic MethodsabstractModern elastic computing systems allow applications to scale up and down automatically, increasing capacity for workload spikes and ensuring cost savings during lulls in activity. Adapting database management systems to work on top of such elastic infrastructure is not a trivial task, and requires a deep understanding of the sophisticated interplay between data fragmentation, replica allocation, and cluster provisioning. This demonstration showcases NashDB, an end-to-end method for addressing these concerns in an automatic way. NashDB relies on economic models to maximize query performance while staying within a user's budget. This demonstration will (1) allow audience members to see how NashDB handles shifting workloads in an adaptive way, and (2) allow audience members to test NashDB themselves by constructing synthetic workloads and seeing how NashDB adapts a cluster to them in real time. Ryan Marcus, Chi Zhang 0068, Geoffrey Kao, Olga Papaemmanouil |
Proc. VLDB Endow. | 4 |