Demo Data from EDBT 2026 / reviewers in the wild / expert
Stefan W. Joseph
dblp:33/4197
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 first-author
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% Data models and query languages · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › evaluation
query performance prediction |
0.0 | 1 | 1991 | A Knowledge-Based Subsystem for a Natural Language Interface to a Database that Predicts and Explains Query Failures · ICDE 1991 |
Query processing and optimization › semantic query processing
semantic query optimization |
0.0 | 1 | 1991 | A Knowledge-Based Subsystem for a Natural Language Interface to a Database that Predicts and Explains Query Failures · ICDE 1991 |
Data models and query languages › natural language interface
natural language interface to database |
0.0 | 1 | 1991 | A Knowledge-Based Subsystem for a Natural Language Interface to a Database that Predicts and Explains Query Failures · ICDE 1991 |
Methods — techniques the papers use, named apart from their topics
semantic constraints · 0.0knowledge-based inference · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | A Knowledge-Based Subsystem for a Natural Language Interface to a Database that Predicts and Explains Query FailuresabstractA practical method is presented for eliminating unnecessary database operations that often arise from poorly posed natural language queries. This subsystem consists mainly of a knowledge-base whose rules are semantic constraints of the database. The inference procedure's actions are, unlike that of similar inference engines, strictly controlled by the structure of the query. Because of this it is easy to implement and it is relatively fast. It is believed that this subsystem represents a practical incremental improvement that can be made to any relational database front end, not just those that must cope with natural language queries. The procedure also has value as part of a practical semantic query optimizer.> Stefan W. Joseph, Romas Aleliunas |
ICDE | 1 |