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Jörg-Uwe Kietz

dblp:82/3738 · DBLP profile ↗
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9ranked-venue papers
6as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 3 first-authorDatabases, data management, data science and information retrieval · 5 · 3 first-authorTheory of computation · 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
2 papers
Knowledge representation and reasoning · 100%
Databases, data mining, and information retrieval
2 papers
Data mining · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational finance and economics · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › logic programming
inductive logic programming
0.011994
An Efficient Subsumption Algorithm for Inductive Logic Programming · ICML 1994
Knowledge, reasoning and agents › Knowledge representation and reasoning
logic programming
0.011994
An Efficient Subsumption Algorithm for Inductive Logic Programming · ICML 1994
Knowledge, reasoning and agents › Knowledge representation and reasoning › description logic
subsumption
0.011994
An Efficient Subsumption Algorithm for Inductive Logic Programming · ICML 1994
Knowledge, reasoning and agents › Knowledge representation and reasoning › concept learning
conceptual clustering
0.011989
A Bootstrapping Approach to Concept Clustering · ML 1989

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

subsumption algorithm · 0.0bootstrapping · 0.0
YearPublicationVenuePosition
2014 "Semantics Inside!" But Let's Not Tell the Data Miners: Intelligent Support for Data Mining
Jörg-Uwe Kietz, Floarea Serban, Simon Fischer 0001, Abraham Bernstein
ESWC1
2002 Learnability of Description Logic Programs
Jörg-Uwe Kietz
ILP1
2001 M4 - A Metamodel for Data Preprocessing
abstract
Metadata-driven tools store control information in repositories that are outside of programs and applications. At runtime, this control information (i.e., metadata) is read, interpreted and dynamically bound into software execution. If new requirements arise, metadata may be changed without affecting the programs sharing it and without requiring re-compilation of these programs. Repositories store metadata according to a metadata structure, called a metamodel. M4 is the metamodel used by Mining Mart, a system for supporting data preprocessing for data mining. The aim of this paper is twofold. First, we introduce M4 (the MetaModel of Mining Mart) and present some ideas underlying the design and implementation. Second, we discuss on the basis of M4 issues related to metadata-driven software: advantages of building and using such software, its weaknesses and the role it plays for metadata management, especially within data warehousing environments.
Anca Vaduva, Jörg-Uwe Kietz, Regina Zücker
DOLAP2
1998 A Data Mining Support Environment and its Application on Insurance Data
Martin Staudt 0001, Jörg-Uwe Kietz, Ulrich Reimer
KDD2
1997 Mining Insurance Data at Swiss Life
Jörg-Uwe Kietz, Ulrich Reimer, Martin Staudt 0001
VLDB1
1994 An Efficient Subsumption Algorithm for Inductive Logic Programming
Jörg-Uwe Kietz, Marcus Lübbe
ICML1
1994 A Polynomial Approach to the Constructive Induction of Structural Knowledge
Jörg-Uwe Kietz, Katharina Morik
Mach. Learn.1
1993 Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
Jörg-Uwe Kietz
ECML1
1989 A Bootstrapping Approach to Concept Clustering
Katharina Morik, Jörg-Uwe Kietz
ML2