VLDB 2026 Research / reviewers in the wild / expert
Claudia Crimi
dblp:33/6904
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1990
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 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.
| Software engineering, system software, and programming languages
1 paper |
Programming languages and type systems · 39% Program analysis · 30% Compilers and program optimization · 30% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization
code generation |
0.0 | 1 | 1990 | Automating Visual Language Generation · IEEE Trans. Software Eng. 1990 |
Program analysis › model inference
grammar inference |
0.0 | 1 | 1990 | Automating Visual Language Generation · IEEE Trans. Software Eng. 1990 |
Programming languages and type systems
visual languages |
0.0 | 1 | 1990 | Automating Visual Language Generation · IEEE Trans. Software Eng. 1990 |
Programming languages and type systems › grammar formalisms
attribute grammars |
0.0 | 1 | 1990 | Automating Visual Language Generation · IEEE Trans. Software Eng. 1990 |
Methods — techniques the papers use, named apart from their topics
grammar inference · 0.0attribute grammar · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1990 | Automating Visual Language GenerationabstractA system to generate and interpret customized visual languages in given application areas is presented. The generation is highly automated. The user presents a set of sample visual sentences to the generator. The generator uses inference grammar techniques to produce a grammar that generalizes the initial set of sample sentences, and exploits general semantic information about the application area to determine the meaning of the visual sentences in the inferred language. The interpreter is modeled on an attribute grammar. A knowledge base, constructed during the generation of the system, is then consulted to construct the meaning of the visual sentence. The architecture of the system and its use in the application environment of visual text editing (inspired by the Heidelberg icon set) enhanced with file management features are reported.> Claudia Crimi, Angela Guercio, Giuliano Pacini, Genny Tortora, Maurizio Tucci |
IEEE Trans. Software Eng. | 1 |