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Andrew Shim

dblp:147/1176 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · unresolved

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
Data integration and cleaning · 50% Query processing and optimization · 50%

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

TopicWeightPapersLastEvidence papers
Data integration and cleaning
data quality
0.212014
iCheck: computationally combating "lies, d-ned lies, and statistics" · SIGMOD Conference 2014
Query processing and optimization
parameterized queries
0.212014
iCheck: computationally combating "lies, d-ned lies, and statistics" · SIGMOD Conference 2014

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

parameter perturbation · 0.2
YearPublicationVenuePosition
2014 iCheck: computationally combating "lies, d-ned lies, and statistics"
abstract
Are you fed up with "lies, d---ned lies, and statistics" made up from data in our media? For claims based on structured data, we present a system to automatically assess the quality of claims (beyond their correctness) and counter misleading claims that cherry-pick data to advance their conclusions. The key insight is to model such claims as parameterized queries and consider how parameter perturbations affect their results. We demonstrate our system on claims drawn from U.S. congressional voting records, sports statistics, and publication records of database researchers.
You Wu 0001, Brett Walenz, Peggy Li, Andrew Shim, Emre Sonmez, Pankaj K. Agarwal, Chengkai Li 0001, Jun Yang 0001, Cong Yu 0001
SIGMOD Conference4