Demo Data from VLDB 2026 / reviewers in the wild / expert
Anita Wasilewska
dblp:73/591
· DBLP profile ↗
7ranked-venue papers
2as first author
0since 2021 · last 2004
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 first-authorHuman-computer interaction and ubiquitous computing · 2 · 1 first-authorTheory of computation · 2Databases, 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.
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% | |
| Theoretical computer science
1 paper |
Logic in computer science · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% | |
| Databases, data mining, and information retrieval
1 paper |
Database theory · 100% |
Topics — the 2 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 › rule learning
inductive learning |
0.0 | 1 | 1993 | Interactive Inductive Learning · Int. J. Man Mach. Stud. 1993 |
Human-robot interaction › robot learning
interactive learning |
0.0 | 1 | 1993 | Interactive Inductive Learning · Int. J. Man Mach. Stud. 1993 |
Methods — techniques the papers use, named apart from their topics
inductive learning · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | Bayesian network multi-classifiers for protein secondary structure prediction
Víctor Robles, Pedro Larrañaga, José M. Peña 0002, Ernestina Menasalvas Ruiz, María S. Pérez 0001, Vanessa Herves, Anita Wasilewska |
Artif. Intell. Medicine | 7 |
| 2002 | Data mining - a semantic modelabstractData mining techniques applied to decision support in real-life problems require a multi-step process. Inputs and outputs of these steps require some standard format to be followed in order to achieve a useful platform for the execution of data mining algorithms. There is a need to develop a uniform model where every operation can be expressed in a standard way, allowing algorithms to cooperate and to reuse results. We present, first, a common structure for the representation of inter-step results, and second, a model of the operator, i.e. the entity that handles and transforms this common structure according to a basic data mining algorithm. Covadonga Fernández 0001, Juan F. Martínez, Anita Wasilewska, Michael Hadjimichael, Ernestina Menasalvas Ruiz |
FUZZ-IEEE | 3 |
| 1996 | Automating Algebraic Proofs in Algebraic LogicabstractWe present here an effective proof theory that allows one to reason within algebras of algebraic logic in a purely syntactic, algebraic fashion. We demonstrate the effectiveness of the method by discussing our automated proofs of problems and theorem Jieh Hsiang, Anita Wasilewska |
Fundam. Informaticae | 2 |
| 1993 | Application of a Rough Set-Based Inductive Learning System
Michael Hadjimichael, Anita Wasilewska |
Fundam. Informaticae | 2 |
| 1993 | Interactive Inductive Learning
Michael Hadjimichael, Anita Wasilewska |
Int. J. Man Mach. Stud. | 2 |
| 1989 | Syntactic Decision Procedures in Information Systems
Anita Wasilewska |
Int. J. Man Mach. Stud. | 1 |
| 1988 | Knowledge Representation Systems: Syntactic Methods
Anita Wasilewska |
IPMU | 1 |