Strategic Term Rewriting and Its Application to a Vdm-sl to Sql Conversion

Alves, Tiago L.; Silva, P. F.; Visser, Joost; Oliveira, José Nuno

doi:10.1007/11526841_27

Cited by 11 publications

(11 citation statements)

References 18 publications

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“…Jointly, the rules for hierarchical-relational data mapping are designed for stepwise elimination of sums, sets, optionals, lists, recursion, and such, in favor of finite maps and products. When applied according to an appropriate strategy, they will lead to a normal form that consists of a product of basic types and maps, which is readily translatable to a relational database schema in SQL [2,15]. There are rules for elimination and distribution, and a particularly challenging rule for recursion elimination, which introduces pointers in the locations of recursive occurrences.…”

Section: Rules For Data Mapping and Format Evolutionmentioning

confidence: 99%

“…In [2] we presented a set of two-level transformation rules that can be combined with combinators presented above into a calculator that automatically converts a hierarchic, possibly recursive data structure to a flat, relational representation. These rules are summarized in abbreviated form in the middle part of Figure 4.…”

Section: Rules For Data Mapping and Format Evolutionmentioning

confidence: 99%

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Coupled Transformation of Schemas, Documents, Queries, and Constraints

Visser

2008

Electronic Notes in Theoretical Computer Science

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Coupled transformation occurs when multiple software artifacts must be transformed in such a way that they remain consistent with each other. For instance, when a database schema is adapted in the context of system maintenance, the persistent data residing in the system's database needs to be migrated to conform to the adapted schema. Also, queries embedded in the application code and any declared referential constraints must be adapted to take the schema changes into account. As another example, in XML-to-relational data mapping, a hierarchical XML Schema is mapped to a relational SQL schema with appropriate referential constraints, and the XML documents and queries are converted into relational data and relational queries. The 2LT project is aimed at providing a formal basis for coupled transformation. This formal basis is found in data refinement theory, point-free program calculation, and strategic term rewriting. We formalize the coupled transformation of a data type by an algebra of information-preserving data refinement steps, each witnessed by appropriate data conversion functions. Refinement steps are modeled by so-called twolevel rewrite rules on type expressions that synthesize conversion functions between redex and reduct while rewriting. Strategy combinators are used to composed two-level rewrite rules into complete rewrite systems. Point-free program calculation is applied to optimized synthesize conversion function, to migrate queries, and to normalize data type constraints. In this paper, we provide an overview of the challenges met by the 2LT project and we give a sketch of the solutions offered.

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Section: Rules For Data Mapping and Format Evolutionmentioning

confidence: 99%

Section: Rules For Data Mapping and Format Evolutionmentioning

confidence: 99%

Coupled Transformation of Schemas, Documents, Queries, and Constraints

Visser

2008

Electronic Notes in Theoretical Computer Science

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“…Grammar convergence relates to mappings in data processing (Thomas, 2003;Lämmel and Meijer, 2006), specifically to the underlying theory of data refinement, and applications thereof (Hoare, 1972;Morgan, 1990;Alves et al, 2005;Oliveira, 2008;Cunha et al, 2008). In data refinement, one also considers certain well-defined operators for transforming data models.…”

Section: Coupled Transformationsmentioning

confidence: 99%

Recovering grammar relationships for the Java Language Specification

Lämmel

Zaytsev²

2010

Software Qual J

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Grammar convergence is a method that helps discovering relationships between different grammars of the same language or different language versions. The key element of the method is the operational, transformation-based representation of those relationships. Given input grammars for convergence, they are transformed until they are structurally equal. The transformations are composed from primitive operators; properties of these operators and the composed chains provide quantitative and qualitative insight into the relationships between the grammars at hand.We describe a refined method for grammar convergence, and we use it in a major study, where we recover the relationships between all the grammars that occur in the different versions of the Java Language Specification (JLS). The relationships are represented as grammar transformation chains that capture all accidental or intended differences between the JLS grammars. This method is mechanized and driven by nominal and structural differences between pairs of grammars that are subject to asymmetric, binary convergence steps.We present the underlying operator suite for grammar transformation in detail, and we illustrate the suite with many examples of transformations on the JLS grammars. We also describe the extraction effort, which was needed to make the JLS grammars amenable to automated processing. We include substantial metadata about the convergence process for the JLS so that the effort becomes reproducible and transparent.

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“…See more details about notation and terminology in section 3. where A is the datatype of data to be represented and C is the chosen datatype of representations 2 .…”

Section: Context and Motivationmentioning

confidence: 99%

“…denote the generic type of a relational database [2]. Every RDBT -compliant tuple db is a collection of n relational tables (index i = 1, n) each of which is a mapping from a tuple of keys (index j) to a tuple of data of interest (index k).…”

Section: Calculating Database Schemes From Abstract Modelsmentioning

confidence: 99%

Transforming Data by Calculation

Oliveira

2008

Lecture Notes in Computer Science

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Abstract. This paper addresses the foundations of data-model transformation. A catalog of data mappings is presented which includes abstraction and representation relations and associated constraints. These are justified in an algebraic style via the pointfree-transform, a technique whereby predicates are lifted to binary relation terms (of the algebra of programming) in a two-level style encompassing both data and operations. This approach to data calculation, which also includes transformation of recursive data models into "flat" database schemes, is offered as alternative to standard database design from abstract models. The calculus is also used to establish a link between the proposed transformational style and bidirectional lenses developed in the context of the classical view-update problem.

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Strategic Term Rewriting and Its Application to a Vdm-sl to Sql Conversion

Cited by 11 publications

References 18 publications

Coupled Transformation of Schemas, Documents, Queries, and Constraints

Coupled Transformation of Schemas, Documents, Queries, and Constraints

Recovering grammar relationships for the Java Language Specification

Transforming Data by Calculation

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