Abstract
Database normalization is a central part of database design in which we re-organise the data stored so as to progressively ensure that as few anomalies occur as possible upon insertions, deletions and/or modifications. Successive normalizations of a database to higher normal forms continue to reduce the potential for such anomalies. We show here that database normalization follows as a consequence (or special case, or by-product) of the Minimum Message Length (MML) principle of machine learning and inductive inference. In other words, someone (previously) oblivious to database normalization but well-versed in MML could examine a database and - using MML considerations alone - normalise it, and even discover the notion of attribute inheritance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Codd, E.: A relational model of data for large shared data banks. Communications of the ACM (1979)
Date, C.: An Introduction to Database Systems. Addison-Wesley Longman, Amsterdam (1999)
William, K.: A simple guide to five normal forms in relational database theory. Communications of the ACM (1983)
Wallace, C.S., Boulton, D.M.: An information measure for classification. Computer Journal 11, 185–194 (1968)
Wallace, C.S., Freeman, P.R.: Estimation and inference by compact coding. Journal of the Royal Statistical Society series B 49(3), 240–252 (1987)
Wallace, C.S., Dowe, D.L.: Minimum message length and Kolmogorov complexity. Computer Journal 42(4), 270–283 (1999)
Wallace, C.S.: Statistical and Inductive Inference by Minimum Message Length. Information Science and Statistics. Springer, Heidelberg (May 2005)
Comley, J.W., Dowe, D.L.: Minimum message length and generalized Bayesian nets with asymmetric languages. In: Grünwald, P., Pitt, M.A., Myung, I.J. (eds.) Advances in Minimum Description Length: Theory and Applications, pp. 265–294. M.I.T. Press, Cambridge (April 2005)
Dowe, D.L.: Foreword re C. S. Wallace. Computer Journal 51(5), 523–560 (2008); Christopher Stewart WALLACE (1933-2004) memorial special issue
Dowe, D.L.: MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness. In: Wood, J., Forster, M.R., Bandyopadhyay, P. (eds.) Handbook of the Philosophy of Science - (HPS Volume 7) Philosophy of Statistics, vol. 7, pp. 861–942. Elsevier, Amsterdam (2010)
Wallace, C.S., Dowe, D.L.: MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing 10, 73–83 (2000)
Dowe, D.L.: Minimum Message Length and statistically consistent invariant (objective?) Bayesian probabilistic inference - from (medical) “evidence”. Social Epistemology 22(4), 433–460 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dowe, D.L., Zaidi, N.A. (2010). Database Normalization as a By-product of Minimum Message Length Inference. In: Li, J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science(), vol 6464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17432-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-17432-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17431-5
Online ISBN: 978-3-642-17432-2
eBook Packages: Computer ScienceComputer Science (R0)