Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Tarik Bouaziz

dblp:47/4206 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › uncertainty reasoning
fuzzy reasoning
0.011998
Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998
Database system architecture and tuning
active database
0.011998
Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998
Data models and query languages › database programming
triggers
0.011998
Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998
Data integration and cleaning
imprecise data
0.011998
Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases · ICDE 1998

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

fuzzy logic · 0.0
YearPublicationVenuePosition
1998 Fuzzy Triggers: Incorporating Imprecise Reasoning into Active Databases
abstract
Traditional 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
ICDE2
1997 Design and Implementation of TEMPO Fuzzy Triggers
Tarik Bouaziz, Janne Karvonen, Anton Pesonen, Antoni Wolski
DEXA1