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Brian Dolan

dblp:76/7259 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
cluster resource management and scheduling
0.412020
Providing SLOs for Resource-Harvesting VMs in Cloud Platforms · OSDI 2020
Cloud and datacenter computing
virtualization
0.112020
Providing SLOs for Resource-Harvesting VMs in Cloud Platforms · OSDI 2020
Data mining
statistical analysis
0.112009
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
YearPublicationVenuePosition
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
OSDI6
2009 MAD Skills: New Analysis Practices for Big Data
abstract
As 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