The motivation of this work is to provide a type-theoretical basis for developing a practical polymorphic programming language with labeled records and labeled variants. Our goal is to establish both a polymorphic type discipline and an efficient compilation method for a calculus with those labeled data structures. We define a second-order, polymorphic record calculus as an extension of Girard-Reynolds polymorphic lambda calculus. We then develop an ML-style type inference algorithm for a predicative subset of the second-order record calculus. The soundness of the type system and the completeness of the type inference algorithm are shown. These results extend Milner's type inference algorithm, Damas and Milner's account of ML's let polymorphism, and Harper and Mitchell's analysis on XMLTo establish an efficient compilation method for the polymorphic record calculus, we first define an implementation calculus, where records are represented as vectors whose elements are accessed by direct indexing, and variants are represented as values tagged with a natural number indicating the position in the vector of functions m a switch statement.We then develop an algorithm to translate the polymorphic record calculus into the implementation calculus using type reformation obtained by the type inference algorithm. The correctness of the compilation algorithm is proved, that is, the compdation algorithm is shown to preserve both typing and the operational behavior of a program. Based on these results, Standard ML has been extended with labeled records, and its compiler has been implemented.

Buneman, P., A. Jung and A. Ohori, Using powerdomains to generalize relational databases, Theoretical Computer Science 91 (1991) 23-55.

We give a framework for denotational semantics for the polymorphic "core" of the programming language ML. This framework requires no more semantic material than what is needed for modeling the simple type discipline. In our view, the terms of ML are pairs consisting of a raw (untyped) lambda term and a typescheme that ML's type inference system can derive for the raw term. We interpret type-schemes as sets of simple types. Then, given any model M of the simply typed lambda calculus, the meaning of an ML term will be a set of pairs, each consisting of a simple type τ and an element of M of type τ.Hence, there is no need to interpret all raw terms, as was done in Milner's original semantic framework. In comparison to Mitchell and Harper's analysis, we avoid having to provide a very large type universe in which generic type-schemes are interpreted. Also, we show how to give meaning to ML terms rather than to derivations in the ML type inference system (which can be several for the same term).We give an axiomatization for the equational theory that corresponds to our semantic framework and prove the analogs of the compeleteness theorems that Friedman proved for the simply typed lambda calculus. The framework can be extended to languages with constants, type constructors and recursive types (via regular trees). For the extended language, we prove a theorem that allows the transfer of certain full abstraction results from languages based on the typed lambda calculus to ML-like languages. We give a framework for denotational semantics for the polymorphic "core" of the programming language ML. This framework requires no more semantic material than what is needed for modeling the simple type discipline. In our view, the terms of ML are pairs consisting of a raw (untyped) lambda term and a type-scheme that ML's type inference system can derive for the raw term. We interpret type-schemes as sets of simple types. Then, given any model M of the simply typed lambda calculus, the meaning of an ML term will be a set of pairs, each consisting of a simple type r and an element of M of type r.Hence, there is no need to interpret all raw terms, as was done in Milner's original semantic framework.In comparison to Mitchell and Harper's analysis, we avoid having to provide a very large type universe in which generic type-schemes are interpreted. Also, we show how to give meaning to ML terms rather than to derivations in the ML type inference system (whic11 can be several for the same term).We give an axiomatization for the equational theory that corresponds to our semantic framework and prove the analogs of the compeleteness theorems that Friedman proved for the simply typed lambda calculus. The framework can be extended to languages with constants, type constructors and recursive types (via regular trees). For the extended language, we prove a theorem that allows the transfer of certain full abstraction results from languages based on the typed lambda calculus to ML-like languages.

We extend an ML-like implicit type system to include a number of structures and operations that are eommon in database programming including set.s, labeled records, joins and projections.We then show that the type inference problem of the system is decidable by extending the notion of principal type schemes to include conditions on substitutions.Combi.ned with Milner's polymorphic let constructor, our language also supports type inheritance.

The polymorphic type system of ML can be extended in two ways that make it appropriate as the basis of a database programming language. The first is an extension to the language of types that captures the polymorphic nature of field selection; the second is a technique that generalizes relational operators to arbitrary data structures. The combination provides a statically typed language in which relational databases may be cleanly represented as typed structures. As in ML types are inferred, which relieves the programmer of making the rather complicated type assertions that may be required to express the most general type of a program that involves field selection and generalized relational operators.It is also possible to use these ideas to implement various aspects of object-oriented databases. By implementing database objects as reference types and generating the appropriate views -sets of structures with "identity" -we can achieve a degree of static type checking for object-oriented databases. Moreover it is possible to exploit the type system to check the consistency of object-oriented classes (abstract data types with inheritance). A prototype language based on these ideas has been implemented. While it lacks some important practical features, it demonstrates that a wide variety of database structures can be cleanly represented in a polymorphic programming language. Atsushi Ohorit Department of Computing ScienceUniversity of Glasgow Glasgow GI2 8QQ, Scotland AbstractThe polymorphic type system of ML can be extended in two ways that make it appropriate as the basis of a database programming language. The first is an extension to the language of types that captures the polymorphic nature of field selection; the second is a technique that generalizes relational operators to arbitrary data structures. The combination provides a statically typed language in which relational databases may be cleanly represented as typed structures. As in ML types are inferred, which relieves the programmer of making the rather complicated type assertions that may be required to express the most general type of a program that involves field selection and generalized relational operators.It is also possible to use these ideas to implement various aspects of object-oriented databases. By implementing database objects as reference types and generating the appropriate views -sets of structures with "identityn -we can achieve a degree of static type checking for object-oriented databases. Moreover it is possible to exploit the type system to check the consistency of object-oriented classes (abstract data types with inheritance). A prototype language based on these ideas has been implemented. While it lacks some important practical features, it demonstrates that a wide variety of database structures can be cleanly represented in a polymorphic programming language.