Introduction to Algorithms
Stochastic Finite Learning of the Pattern Languages
Machine Learning
Expressiveness and complexity of XML Schema
ACM Transactions on Database Systems (TODS)
Inferring XML schema definitions from XML data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Developments from enquiries into the learnability of the pattern languages from positive data
Theoretical Computer Science
Algorithms for learning regular expressions from positive data
Information and Computation
Inference of concise regular expressions and DTDs
ACM Transactions on Database Systems (TODS)
Learning Deterministic Regular Expressions for the Inference of Schemas from XML Data
ACM Transactions on the Web (TWEB)
Complexity of Decision Problems for XML Schemas and Chain Regular Expressions
SIAM Journal on Computing
Inferring descriptive generalisations of formal languages
Journal of Computer and System Sciences
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We study the problem of generalizing from a finite sample to a language taken from a predefined language class. The two language classes we consider are subsets of the regular languages and have significance in the specification of XML documents (the classes corresponding to so called chain regular expressions, Chares, and to single occurrence regular expressions, Sores). The previous literature gave a number of algorithms for generalizing to Sores providing a trade off between quality of the solution and speed. Furthermore, a fast but non-optimal algorithm for generalizing to Chares is known. For each of the two language classes we give an efficient algorithm returning a minimal generalization from the given finite sample to an element of the fixed language class; such generalizations are called descriptive. In this sense, both our algorithms are optimal.