EDBT 2026 Demo / reviewers in the wild / expert
Brian Dolan
dblp:76/7259
· 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
Software engineering, systems software and programming languages · 1Databases, 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% | |
| Databases, data mining, and information retrieval
1 paper |
Database system architecture and tuning · 50% Data mining · 50% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
cluster resource management and scheduling |
0.4 | 1 | 2020 | Providing SLOs for Resource-Harvesting VMs in Cloud Platforms · OSDI 2020 |
Cloud and datacenter computing
virtualization |
0.1 | 1 | 2020 | Providing SLOs for Resource-Harvesting VMs in Cloud Platforms · OSDI 2020 |
Data mining
statistical analysis |
0.1 | 1 | 2009 | MAD Skills: New Analysis Practices for Big Data · Proc. VLDB Endow. 2009 |
Methods — techniques the papers use, named apart from their topics
mapreduce · 0.1SQL · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Providing SLOs for Resource-Harvesting VMs in Cloud Platforms
Lurdh Pradeep Reddy Ambati, Íñigo Goiri, Felipe Vieira Frujeri, Alper Gun, Brian Dolan, Brian Corell, Sekhar Pasupuleti, Thomas Moscibroda, Sameh Elnikety, Marcus Fontoura, Ricardo Bianchini |
OSDI | 6 |
| 2009 | MAD Skills: New Analysis Practices for Big DataabstractAs massive data acquisition and storage becomes increasingly affordable, a wide variety of enterprises are employing statisticians to engage in sophisticated data analysis. In this paper we highlight the emerging practice of Magnetic, Agile, Deep (MAD) data analysis as a radical departure from traditional Enterprise Data Warehouses and Business Intelligence. We present our design philosophy, techniques and experience providing MAD analytics for one of the world's largest advertising networks at Fox Audience Network, using the Greenplum parallel database system. We describe database design methodologies that support the agile working style of analysts in these settings. We present dataparallel algorithms for sophisticated statistical techniques, with a focus on density methods. Finally, we reflect on database system features that enable agile design and flexible algorithm development using both SQL and MapReduce interfaces over a variety of storage mechanisms. Jeffrey Cohen, Brian Dolan, Mark Dunlap, Joseph M. Hellerstein, Caleb Welton |
Proc. VLDB Endow. | 2 |