Demo Data from EDBT 2026 / reviewers in the wild / expert
Vera Lychagina
dblp:52/10355
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Query processing and optimization › SQL query processing
SQL query engine |
0.4 | 1 | 2019 | Procella: Unifying serving and analytical data at YouTube · Proc. VLDB Endow. 2019 |
Distributed and cloud data management › mapreduce
hybrid SQL-MapReduce system |
0.1 | 1 | 2011 | Tenzing A SQL Implementation On The MapReduce Framework · Proc. VLDB Endow. 2011 |
High-performance computing › data-intensive computing
large-scale data processing |
0.0 | 1 | 2011 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Procella: Unifying serving and analytical data at YouTubeabstractLarge 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 |