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Types from Data: Making Structured Data First-class Citizens in F#

Petricek, Tomas and Guerra, Gustavo and Syme, Don (2016) Types from Data: Making Structured Data First-class Citizens in F#. In: Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation. PLDI Programming Language Design and Implementation . ACM, New York, USA, pp. 477-490. ISBN 978-1-4503-4261-2. (doi:10.1145/2908080.2908115)

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http://dx.doi.org/10.1145/2908080.2908115

Abstract

Most modern applications interact with external services and access data in structured formats such as XML, JSON and CSV. Static type systems do not understand such formats, often making data access more cumbersome. Should we give up and leave the messy world of external data to dynamic typing and runtime checks? Of course, not! We present F# Data, a library that integrates external structured data into F#. As most real-world data does not come with an explicit schema, we develop a shape inference algorithm that infers a shape from representative sample documents. We then integrate the inferred shape into the F# type system using type providers. We formalize the process and prove a relative type soundness theorem. Our library significantly reduces the amount of data access code and it provides additional safety guarantees when contrasted with the widely used weakly typed techniques.

Item Type: Book section
DOI/Identification number: 10.1145/2908080.2908115
Uncontrolled keywords: F#, Inference, JSON, Type Providers, XML
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Programming Languages and Systems Group
Depositing User: Tomas Petricek
Date Deposited: 25 May 2018 16:39 UTC
Last Modified: 24 Sep 2019 08:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67140 (The current URI for this page, for reference purposes)
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