VLDB 2026 Research / reviewers in the wild / expert
Sridatta Chegu
dblp:181/5715
· DBLP profile ↗
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 |
Query processing and optimization · 44% Information retrieval · 44% Database system architecture and tuning · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › web search
data freshness |
0.2 | 1 | 2016 | Shasta: Interactive Reporting At Scale · SIGMOD Conference 2016 |
Query processing and optimization
online query processing |
0.2 | 1 | 2016 | Shasta: Interactive Reporting At Scale · SIGMOD Conference 2016 |
Methods — techniques the papers use, named apart from their topics
query transformation · 0.2join processing over many tables · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Shasta: Interactive Reporting At ScaleabstractWe describe Shasta, a middleware system built at Google to support interactive reporting in complex user-facing applications related to Google's Internet advertising business. Shasta targets applications with challenging requirements: First, user query latencies must be low. Second, underlying transactional data stores have complex "read-unfriendly" schemas, placing significant transformation logic between stored data and the read-only views that Shasta exposes to its clients. This transformation logic must be expressed in a way that scales to large and agile engineering teams. Finally, Shasta targets applications with strong data freshness requirements, making it challenging to precompute query results using common techniques such as ETL pipelines or materialized views. Instead, online queries must go all the way from primary storage to user-facing views, resulting in complex queries joining 50 or more tables. Gokul Nath Babu Manoharan, Stephan Ellner, Karl Schnaitter, Sridatta Chegu, Alejandro Estrella-Balderrama, Stephan Gudmundson, Apurv Gupta, Ben Handy, Bart Samwel, Chad Whipkey, Larysa Aharkava, Himani Apte, Nitin Gangahar, Shivakumar Venkataraman, Divyakant Agrawal, Jeffrey D. Ullman |
SIGMOD Conference | 4 |