Demo Data from EDBT 2026 / reviewers in the wild / expert
Robert Zembowicz
dblp:19/2401
· DBLP profile ↗
9ranked-venue papers
3as first author
0since 2021 · last 1997
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 3 first-authorDatabases, data management, data science and information retrieval · 2Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorTheory of computation · 1
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
2 papers |
Data mining · 85% Machine learning and data management · 15% | |
| Artificial intelligence
1 paper |
Knowledge representation and reasoning · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% | |
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% |
Topics — the 2 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining
pattern mining |
0.0 | 1 | 1996 | Automated Pattern Mining with a Scale Dimension · KDD 1996 |
Computational science and engineering › scientific machine learning
equation discovery |
0.0 | 1 | 1992 | Discovery of Equations: Experimental Evaluation of Convergence · AAAI 1992 |
Methods — techniques the papers use, named apart from their topics
experimental evaluation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | Contigency Tables as the Foundation for Concepts, Concept Hierarchies and Rules: The 49er System ApproachabstractWe analyze relationships between different forms of knowledge that can be discovered in the same data matrix (relational table): contingency tables, equations, concept definitions, concept hierarchies, and rules. We argue that contingency tables are Jan M. Zytkow, Robert Zembowicz |
Fundam. Informaticae | 2 |
| 1996 | Search for Patterns at Each Scale in Massive Data
Jan M. Zytkow, Robert Zembowicz |
ISMIS | 2 |
| 1996 | Automated Pattern Mining with a Scale Dimension
Jan M. Zytkow, Robert Zembowicz |
KDD | 2 |
| 1993 | Recognition of Functional Dependencies in Data
Robert Zembowicz, Jan M. Zytkow |
ISMIS | 1 |
| 1993 | Database Exploration in Search of Regularities
Jan M. Zytkow, Robert Zembowicz |
J. Intell. Inf. Syst. | 2 |
| 1992 | Discovery of Equations: Experimental Evaluation of Convergence
Robert Zembowicz, Jan M. Zytkow |
AAAI | 1 |
| 1992 | Operational Definition Refinement: A Discovery Process
Jan M. Zytkow, Jieming Zhu, Robert Zembowicz |
AAAI | 3 |
| 1992 | The First Phase of Real-World Discovery: Determining Repeatability and Error of Experiments
Jan M. Zytkow, Jieming Zhu, Robert Zembowicz |
ML | 3 |
| 1991 | Automated Discovery of Empirical Equations from Data
Robert Zembowicz, Jan M. Zytkow |
ISMIS | 1 |