Interactive theory revision: an inductive logic programming approach
Interactive theory revision: an inductive logic programming approach
Object orientation in multidatabase systems
ACM Computing Surveys (CSUR)
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Research problems in data warehousing
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Inductive logic programming and knowledge discovery in databases
Advances in knowledge discovery and data mining
Algorithmic Program DeBugging
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Statistical Themes and Lessons for Data Mining
Data Mining and Knowledge Discovery
Using a Relational Database to Support Explanation in a Knowledge-Based System
IEEE Transactions on Knowledge and Data Engineering
Conceptual Database Evolution Through Learning in Object Databases
IEEE Transactions on Knowledge and Data Engineering
The Role of Polymorphic Reuse Mechanisms in Schema Evolution in an Object-Oriented Database
IEEE Transactions on Knowledge and Data Engineering
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
Frog and Turtle: Visual Bridges Between Files and Object-Oriented Data
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
A Model for Classification Structures with Evolution Control
ER '96 Proceedings of the 15th International Conference on Conceptual Modeling
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
The Sequoia 2000 Architecture and Implementation Strategy
The Sequoia 2000 Architecture and Implementation Strategy
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In the last decades, the execution of various scientific experiments aiming at a more comprehensive understanding of our environment, has shown a tremendous increase in data production. Database models provide a more or less adequate mechanism for mapping real--world applications into a computer--bound reality. Since scientific knowledge can be modelled a--priori only to some extent, the question which arises here is how able is a database schema to evolve. On the other side, knowledge can be provided by the underlying scientific data on which data mining algorithms are applied. The main question which arises here is how to provide a suitable environment in order to accomodate the results coming out from data analysis tasks and how these tasks can be supported by a database model.