Demo Data from VLDB 2026 / reviewers in the wild / expert
Byeongguk Kwak
dblp:394/9471
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
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 |
Information retrieval · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Program synthesis and code generation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › query formulation
query synthesis |
0.9 | 1 | 2025 | Synthesizing Document Database Queries Using Collection Abstractions · ICSE 2025 |
Program synthesis and code generation
programming by example |
0.3 | 1 | 2025 | Synthesizing Document Database Queries Using Collection Abstractions · ICSE 2025 |
Methods — techniques the papers use, named apart from their topics
domain-specific language · 1.7collection abstraction · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Synthesizing Document Database Queries Using Collection AbstractionsabstractDocument databases are increasingly popular in various applications, but their queries are challenging to write due to the flexible and complex data model underlying document databases. This paper presents a synthesis technique that aims to generate document database queries from input-output examples automatically. A new domain-specific language is designed to express a representative set of document database queries in an algebraic style. Furthermore, the synthesis technique leverages a novel abstraction of collections for deduction to efficiently prune the search space and quickly generate the target query. An evaluation of 110 benchmarks from various sources shows that the proposed technique can synthesize 108 benchmarks successfully. On average, the synthesizer can generate document database queries from a small number of input-output examples within tens of seconds. Qikang Liu, Yanwen Cai, Byeongguk Kwak, Yuepeng Wang 0001 |
ICSE | 4 |