Functional and Efficient Query Interpreters: Principle, Application and Performances’ Comparison

This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries

Categories for (Big) Data models and optimization

Abstract This paper proposes a theoretical foundation for Big Data. More precisely, it explains how “functors”, a concept coming from Category Theory, can serve to model the various data structures commonly used to represent (large) data sets, and how “natural transformations” can formalize relations between these structures. Algorithms, such as querying a precise information, mainly depend on the data structure considered, and thus natural transformations can serve to optimize these algorithms and get a result in a shorter time. The paper details four functors modeling tabular data, graph structures (e.g. triple stores), cached and split data. Next, the paper explains how, by considering a functional programming language, the concepts can be implemented without effort to propose new tools (e.g. efficient information servers and query languages). And, as a complement to the mathematical models proposed, the paper also presents a optimized data server and a specific query language (based on “unification” to facilitates the search of information). Finally, the paper gives a comparison study and shows that this tool is more efficient than most of the standards available in the market: the functional server appears to be 10+ times faster than relational or document oriented databases (Mysql and MongoDB), and 100+ times faster than a graph database (Neo4j)