Inductive characterisation of database relations

The general claims of this paper are twofold: there are challenging problems for Machine Learning in the field of Databases, and the study of these problems leads to a deeper understanding of Machine Learning. To support the first claim, we consider the problem of characterising a database relation in terms of high-level properties, i.e. attribute dependencies. The problem is reformulated to reveal its inductive nature. To support the second claim, we show that the problems presented here do not fit well into the current framework for inductive learning, and we discuss the outline of a more general theory of inductive learning.

[1]  Carl H. Smith,et al.  Inductive Inference: Theory and Methods , 1983, CSUR.

[2]  David Maier,et al.  The Theory of Relational Databases , 1983 .

[3]  L.P.J. Veelenturf,et al.  Concept learning from examples : Theoretical foundations , 1989 .

[4]  Jack Minker,et al.  Logic and Databases: A Deductive Approach , 1984, CSUR.

[5]  W. W. Armstrong,et al.  Dependency Structures of Data Base Relationships , 1974, IFIP Congress.

[6]  Catriel Beeri,et al.  The Implication Problem for Data Dependencies , 1981, ICALP.

[7]  Ronald Fagin,et al.  An Equivalence Between Relational Database Dependencies and a Fragment of Propositional Logic , 1981, JACM.

[8]  E. Mark Gold,et al.  Language Identification in the Limit , 1967, Inf. Control..

[9]  Ehud Shapiro,et al.  Inductive Inference of Theories from Facts , 1991, Computational Logic - Essays in Honor of Alan Robinson.

[10]  Ehud Shapiro,et al.  Algorithmic Program Debugging , 1983 .

[11]  Richard Statman,et al.  On the Structure of Armstrong Relations for Functional Dependencies , 1984, JACM.

[12]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[13]  Philip D. Laird Inductive Inference by Refinement , 1986, AAAI.

[14]  Michael R. Genesereth,et al.  Logical foundations of artificial intelligence , 1987 .

[15]  John Grant,et al.  On the family of generalized dependency constraints , 1982, JACM.

[16]  P. Laird Learning from Good and Bad Data , 1988 .

[17]  Heikki Mannila,et al.  Design by Example: An Application of Armstrong Relations , 1986, J. Comput. Syst. Sci..