Google’s MapReduce programming model serves for processing large data sets in a massively parallel manner. We deliver the first rigorous description of the model including its advancement as Google’s domain-specific language Sawzall. To this end, we reverse-engineer the seminal papers on MapReduce and Sawzall, and we capture our findings as an executable specification. We also identify and resolve some obscurities in the informal presentation given in the seminal papers. We use typed functional programming (specifically Haskell) as a tool for design recovery and executable specification. Our development comprises three components: (i) the basic program skeleton that underlies MapReduce computations; (ii) the opportunities for parallelism in executing MapReduce computations; (iii) the fundamental characteristics of Sawzall’s aggregators as an advancement of the MapReduce approach. Our development does not formalize the more implementational aspects of an actual, distributed execution of MapReduce computations.

A type-indexed function is a function that is defined for each member of some family of types. Haskell's type class mechanism provides collections of open type-indexed functions, in which the indexing family can be extended by defining a new type class instance but the collection of functions is fixed. The purpose of this paper is to present TypeCase: a design pattern that allows the definition of closed type-indexed functions, in which the index family is fixed but the collection of functions is extensible. It is inspired by Cheney and Hinze's work on lightweight approaches to generic programming. We generalise their techniques as a design pattern. Furthermore, we show that type-indexed functions with typeindexed types, and consequently generic functions with generic types, can also be encoded in a lightweight manner, thereby overcoming one of the main limitations of the lightweight approaches.

The ITERATOR pattern gives a clean interface for element-by-element access to a collection. Imperative iterations using the pattern have two simultaneous aspects: mapping and accumulating. Various existing functional iterations model one or other of these, but not both simultaneously. We argue that McBride and Paterson’s idioms, and in particular the corresponding traverse operator, do exactly this, and therefore capture the essence of the ITERATOR pattern. We present some axioms for traversal, and illustrate with a simple example, the repmin problem.

Coupled transformations occur in software evolution when multiple artifacts must be modified in such a way that they remain consistent with each other. An important example involves the coupled transformation of a data type, its instances, and the programs that consume or produce it. Previously, we have provided a formal treatment of transformation of the first two: data types and instances. The treatment involved the construction of typesafe, type-changing strategic rewrite systems. In this paper, we extend our treatment to the transformation of corresponding data processing programs. The key insight underlying the extension is that both data migration functions and data processors can be represented type-safely by a generalized abstract data type (GADT). These representations are then subjected to program calculation rules, harnessed in typesafe, type-preserving strategic rewrite systems. For ease of calculation, we use point-free representations and corresponding calculation rules. Thus, coupled transformations are carried out in two steps. First, a type-changing rewrite system is applied to a source type to obtain a target type together with (representations of) migration functions between source and target. Then, a type-preserving rewrite system is applied to the composition of a migration function and a data processor on the source (or target) type to obtain a data processor on the target (or source) type. All rewrites are type-safe. Key words: Program transformation, term rewriting, strategic programming, generalized abstract datatypes, data refinement.

We give a necessary and sufficient condition for when a set-theoretic function can be written using the recursion operator fold, and a dual condition for the recursion operator unfold. The conditions are simple, practically useful, and generic in the underlying datatype. 1

Abstract. The subject of this paper is functional program transformation in the so-called point-free style. By this we mean first translating programs to a form consisting only of categorically-inspired combinators, algebraic data types defined as fixed points of functors, and implicit recursion through the use of type-parameterized recursion patterns. This form is appropriate for reasoning about programs equationally, but difficult to actually use in practice for programming. In this paper we present a collection of libraries and tools developed at Minho with the aim of supporting the automatic conversion of programs to point-free (embedded in Haskell), their manipulation and rule-driven simplification, and the (limited) automatic application of fusion for program transformation. 1

Unfolds generate data structures, and folds consume them.

Abstract. Lenses are one the most popular approaches to define bidirectional transformations between data models. A bidirectional transformation with view-update, denoted a lens, encompasses the definition of a forward transformation projecting concrete models into abstract views, together with a backward transformation instructing how to translate an abstract view to an update over concrete models. In this paper we show that most of the standard point-free combinators can be lifted to lenses with suitable backward semantics, allowing us to use the point-free style to define powerful bidirectional transformations by composition. We also demonstrate how to define generic lenses over arbitrary inductive data types by lifting standard recursion patterns, like folds or unfolds. To exemplify the power of this approach, we “lensify ” some standard functions over naturals and lists, which are tricky to define directly “by-hand ” using explicit recursion.

Dynamic programming is an algorithm design technique, which allows to improve efficiency by avoiding re-computation of identical subtasks. We present a new recursion combinator, dynamorphism,which captures the dynamic programming recursion pattern with memoization and identify some simple conditions when functions defined by structured general recursion can be redefined as a dynamorphism. The applicability of the new recursion combinator is demonstrated on classical dynamic programming algorithms: Fibonacci numbers, binary partitions, edit distance and longest common subsequence.

Defining functions over large, possibly recursive, data structures usually involves a lot of boilerplate. This code simply traverses non-interesting parts of the data, and rapidly becomes a maintainability problem. Many generic programming libraries have been proposed to address this issue. Most of them allow the user to specify the behavior just for the interesting bits of the structure, and provide traversal combinators to “scrap the boilerplate”. The expressive power of these libraries usually comes at the cost of efficiency, since runtime checks are used to detect where to apply the type-specific behavior. In previous work we have developed an effective rewrite system for specialization and optimization of generic programs. In this paper we extend it to also cover recursive data types. The key idea is to specialize traversal combinators using well-known recursion patterns, such as folds or paramorphisms. These are ruled by a rich set of algebraic laws that enable aggressive optimizations. We present a type-safe encoding of this rewrite system in Haskell, based on recent language extensions such as type-indexed type families. Keywords: