Demo Data from EDBT 2026 / reviewers in the wild / expert
Timothy Tan
dblp:233/4067
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—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.
| Databases, data mining, and information retrieval
1 paper |
Graph data management · 50% Distributed and cloud data management · 50% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Interconnection networks and networks-on-chip · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Graph data management › graph database
distributed graph database |
0.4 | 1 | 2020 | A1: A Distributed In-Memory Graph Database · SIGMOD Conference 2020 |
Distributed and cloud data management
distributed query processing |
0.4 | 1 | 2020 | A1: A Distributed In-Memory Graph Database · SIGMOD Conference 2020 |
Interconnection networks and networks-on-chip
remote direct memory access |
0.1 | 1 | 2020 | A1: A Distributed In-Memory Graph Database · SIGMOD Conference 2020 |
Methods — techniques the papers use, named apart from their topics
RDMA · 0.9FaRM · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | A1: A Distributed In-Memory Graph DatabaseabstractA1 is an in-memory distributed database used by the Bing search engine to support complex queries over structured data. The key enablers for A1 are availability of cheap DRAM and high speed RDMA (Remote Direct Memory Access) networking in commodity hardware. A1 uses FaRM [11,12] as its underlying storage layer and builds the graph abstraction and query engine on top. The combination of in-memory storage and RDMA access requires rethinking how data is allocated, organized and queried in a large distributed system. A single A1 cluster can store tens of billions of vertices and edges and support a throughput of 350+ million of vertex reads per second with end to end query latency in single digit milliseconds. In this paper we describe the A1 data model, RDMA optimized data structures and query execution. Chiranjeeb Buragohain, Knut Magne Risvik, Paul Brett, Miguel Castro 0001, Wonhee Cho 0004, Joshua Cowhig, Nikolas Gloy, Karthik Kalyanaraman, Richendra Khanna, John Pao, Matthew Renzelmann, Alex Shamis, Timothy Tan, Shuheng Zheng |
SIGMOD Conference | 13 |