Demo Data from VLDB 2026 / reviewers in the wild / expert
Tarik Bouaziz
dblp:47/4206
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 1998
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 2 · 1 first-authorArtificial intelligence and machine learning · 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 |
Database system architecture and tuning · 44% Data models and query languages · 44% Data integration and cleaning · 13% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Knowledge representation and reasoning › uncertainty reasoning
fuzzy reasoning |
0.0 | 1 | 1998 | Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998 |
Database system architecture and tuning
active database |
0.0 | 1 | 1998 | Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998 |
Data models and query languages › database programming
triggers |
0.0 | 1 | 1998 | Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998 |
Data integration and cleaning
imprecise data |
0.0 | 1 | 1998 | Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998 |
Methods — techniques the papers use, named apart from their topics
fuzzy logic · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1998 | Fuzzy Triggers: Incorporating Imprecise Reasoning into Active DatabasesabstractTraditional event-condition-action triggers (active database rules) include a Boolean predicate as a trigger condition. We propose fuzzy triggers whereby fuzzy inference is utilized in the condition evaluation. In this way, approximate reasoning may be integrated with a traditional crisp database. The new approach paves the way for intuitive expression of application semantics of imprecise nature, in database-bound applications. Two fuzzy trigger models are proposed. Firstly, a set of fuzzy rules is encapsulated into a Boolean-valued function called a rule set function, leading to the C-fuzzy trigger model. Subsequently, actions are expressed also in fuzzy terms, and the corresponding CA-fuzzy trigger model is proposed. Examples are provided to illustrate how fuzzy triggers can be applied to a real-life drive control system in an industrial installation. Antoni Wolski, Tarik Bouaziz |
ICDE | 2 |
| 1997 | Design and Implementation of TEMPO Fuzzy Triggers
Tarik Bouaziz, Janne Karvonen, Anton Pesonen, Antoni Wolski |
DEXA | 1 |