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Doug Bateman

dblp:181/5719 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2016
—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
Database system architecture and tuning · 77% Query processing and optimization · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization › query optimization › algebraic query optimization
relational algebra optimization		 0.112016
Introduction to Spark 2.0 for Database Researchers · SIGMOD Conference 2016

Methods — techniques the papers use, named apart from their topics

whole-stage code generation · 0.2catalyst optimizer · 0.2
YearPublicationVenuePosition
2016 Introduction to Spark 2.0 for Database Researchers
abstract
Originally started as an academic research project at UC Berkeley, Apache Spark is one of the most popular open source projects for big data analytics. Over 1000 volunteers have contributed code to the project; it is supported by virtually every commercial vendor; many universities are now offering courses on Spark. Spark has evolved significantly since the 2010 research paper: its foundational APIs are becoming more relational and structural with the introduction of the Catalyst relational optimizer, and its execution engine is developing quickly to adopt the latest research advances in database systems such as whole-stage code generation.
Michael Armbrust, Doug Bateman, Reynold Xin, Matei Zaharia
 SIGMOD Conference2