Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Vera Lychagina

dblp:52/10355 · DBLP profile ↗
← Back
2ranked-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 · 2

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
2 papers
Database system architecture and tuning · 38% Query processing and optimization · 38% Distributed and cloud data management · 25%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Cloud and datacenter computing · 75% High-performance computing · 25%

Topics — the 3 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Query processing and optimization › SQL query processing
SQL query engine
0.412019
Procella: Unifying serving and analytical data at YouTube · Proc. VLDB Endow. 2019
Distributed and cloud data management › mapreduce
hybrid SQL-MapReduce system
0.112011
Tenzing A SQL Implementation On The MapReduce Framework · Proc. VLDB Endow. 2011
High-performance computing › data-intensive computing
large-scale data processing
0.012011
Tenzing A SQL Implementation On The MapReduce Framework · Proc. VLDB Endow. 2011

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

mapreduce · 0.2
YearPublicationVenuePosition
2019 Procella: Unifying serving and analytical data at YouTube
abstract
Large organizations like YouTube are dealing with exploding data volume and increasing demand for data driven applications. Broadly, these can be categorized as: reporting and dashboarding, embedded statistics in pages, time-series monitoring, and ad-hoc analysis. Typically, organizations build specialized infrastructure for each of these use cases. This, however, creates silos of data and processing, and results in a complex, expensive, and harder to maintain infrastructure. At YouTube, we solved this problem by building a new SQL query engine - Procella. Procella implements a superset of capabilities required to address all of the four use cases above, with high scale and performance, in a single product. Today, Procella serves hundreds of billions of queries per day across all four workloads at YouTube and several other Google product areas.
Biswapesh Chattopadhyay, Priyam Dutta, Ott Tinn, Andrew McCormick, Aniket Mokashi, Paul Harvey 0002, Hector Gonzalez, David Lomax, Sagar Mittal, Roee Ebenstein, Nikita Mikhaylin, Hung-Ching Lee, Tony Xu, Luis Perez, Farhad Shahmohammadi, Tran Bui, Neil Mckay, Selcuk Aya, Vera Lychagina, Brett Elliott
Proc. VLDB Endow.21
2011 Tenzing A SQL Implementation On The MapReduce Framework
Biswapesh Chattopadhyay, Sagar Mittal, Prathyusha Aragonda, Vera Lychagina, Younghee Kwon
Proc. VLDB Endow.6