There are different notions of computation, the most popular being monads, applicative functors, and arrows. In this article, we show that these three notions can be seen as instances of a unifying abstract concept: monoids in monoidal categories. We demonstrate that even when working at this high level of generality, one can obtain useful results. In particular, we give conditions under which one can obtain free monoids and Cayley representations at the level of monoidal categories, and we show that their concretisation results in useful constructions for monads, applicative functors, and arrows. Moreover, by taking advantage of the uniform presentation of the three notions of computation, we introduce a principled approach to the analysis of the relation between them.

Cuneiform is a minimal functional programming language for large-scale scientific data analysis. Implementing a strict black-box view on external operators and data, it allows the direct embedding of code in a variety of external languages like Python or R, provides data-parallel higher order operators for processing large partitioned data sets, allows conditionals and general recursion, and has a naturally parallelizable evaluation strategy suitable for multi-core servers and distributed execution environments like Hadoop, HTCondor, or distributed Erlang. Cuneiform has been applied in several data-intensive research areas including remote sensing, machine learning, and bioinformatics, all of which critically depend on the flexible assembly of pre-existing tools and libraries written in different languages into complex pipelines. This paper introduces the computation semantics for Cuneiform. It presents Cuneiform's abstract syntax, a simple type system, and the semantics of evaluation. Providing an unambiguous specification of the behavior of Cuneiform eases the implementation of interpreters which we showcase by providing a concise reference implementation in Erlang. The similarity of Cuneiform's syntax to the simply typed lambda calculus puts Cuneiform in perspective and allows a straightforward discussion of its design in the context of functional programming. Moreover, the simple type system allows the deduction of the language's safety up to black-box operators. Last, the formulation of the semantics also permits the verification of compilers to and from other workflow languages.

We present the starting elements of a mathematical theory of policy advice and avoidability. More specifically, we formalize a cluster of notions related to policy advice, such as policy, viability, reachability, and propose a novel approach for assisting decision making, based on the concept of avoidability. We formalize avoidability as a relation between current and future states, investigate under which conditions this relation is decidable and propose a generic procedure for assessing avoidability. The formalization is constructive and makes extensive use of the correspondence between dependent types and logical propositions, decidable judgments are obtained through computations. Thus, we aim for a computational theory, and emphasize the role that computer science can play in global system science.

In this paper, we develop an algebraic approach to data integration by combining techniques from functional programming, category theory, and database theory. In our formalism, database schemas and instances are algebraic (multi-sorted equational) theories of a certain form. Schemas denote categories, and instances denote their initial (term) algebras. The instances on a schema S form a category, S–Inst, and a morphism of schemas F : S → T induces three adjoint data migration functors: ΣF : S–Inst → T–Inst, defined by substitution along F, which has a right adjoint ΔF : T–Inst → S–Inst, which in turn has a right adjoint ΠF : S–Inst → T–Inst. We present a query language based on for/where/return syntax where each query denotes a sequence of data migration functors; a pushout-based design pattern for performing data integration using our formalism; and describe the implementation of our formalism in a tool we call AQL (Algebraic Query Language).

Many students complete PhDs in functional programming each year. As a service to the community, the Journal of Functional Programming publishes the abstracts from PhD dissertations completed during the previous year.

One key aspect of data-centric applications is the manipulation of data stored in persistent repositories, which is moving fast from querying a centralized relational database to the ad-hoc combination of constellations of data sources. The extension of general purpose languages with query operations is increasingly popular, as a tool to improve reasoning and optimizing capabilities of interpreters and compilers. However, not much is being done to integrate and orchestrate different and separate sources of data. We present a data manipulation language that abstracts the nature and location of data-sources. We define its semantics and a type directed query localization mechanism to be used in development tools for heterogeneous environments to efficiently compile them into native queries. We introduce a localization procedure based on rewriting of query expressions that is confluent, terminating and provides the maximum mapping between site capabilities and the structure of the query. We provide formal type safety results that support the sound distribution of query fragments over remote sites. Our approach is also suitable for an interactive query construction environment by rich user interfaces that provide immediate feedback on data manipulation operations. This approach is currently the base for the data layer of a development platform for mobile and web applications.

We present an algebra for data-intensive scalable computing based on monoid homomorphisms that consists of a small set of operations that capture most features supported by current domain-specific languages for data-centric distributed computing. This algebra is being used as the formal basis of MRQL, which is a query processing and optimization system for large-scale distributed data analysis. The MRQL semantics is given in terms of monoid comprehensions, which support group-by and order-by syntax and can work on heterogeneous collections without requiring any extension to the monoid algebra. We present the syntax and semantics of monoid comprehensions and provide rules to translate them to the monoid algebra. We give evidence of the effectiveness of our algebra by presenting some important optimization rules, such as converting nested queries to joins.