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Tiangang Chen

dblp:273/7168 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—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 · 77% Data integration and cleaning · 23%

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

TopicWeightPapersLastEvidence papers
Query processing and optimization
query debugging
0.412020
I-Rex: An Interactive Relational Query Explainer for SQL · Proc. VLDB Endow. 2020
Data integration and cleaning
data provenance
0.112020
I-Rex: An Interactive Relational Query Explainer for SQL · Proc. VLDB Endow. 2020

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

provenance · 0.4counterexample generation · 0.4
YearPublicationVenuePosition
2020 I-Rex: An Interactive Relational Query Explainer for SQL
abstract
We demonstrate I-Rex 1 , a system designed to help users understand SQL query evaluation and debug SQL queries. I-Rex lets users interactively "trace" the evaluation of complex SQL queries, including those with correlated subqueries. I-Rex also explains why a query returns an incorrect answer with respect to a reference query over a test database instance---a common use case in education and software regression testing. To avoid the cognitive overload caused by debugging over a large database instance, I-Rex lets users focus on smaller instances contained in the large one (which we call "counterexamples") that still distinguish the two queries. Supporting these features for SQL queries poses two key challenges. First, unlike debugging for procedural languages, it is not clear how to trace a declarative SQL query, because its execution plan often differs from how it was originally written. I-Rex offers a novel interface for tracing SQL query evaluation in a way faithful to how queries are written syntactically, even for complex queries involving multiple levels of nesting and correlation. Second, we need a method for finding small counterexamples that handles the complexity of practical SQL. I-Rex extends provenance support for SQL in non-trivial ways to work with various query constructs. This demonstration walks through use cases in which I-Rex helps users understand and debug SQL queries.
Zhengjie Miao, Tiangang Chen, Alexander Bendeck, Kevin Day, Sudeepa Roy 0001, Jun Yang 0001
Proc. VLDB Endow.2