FLANDM: a development framework of domain-specific languages for data mining democratisation. (December 2018)

• Record Type: Journal Article
• Authors: de la Vega, Alfonso; García-Saiz, Diego; Zorrilla, Marta; Sánchez, Pablo
• Is Part Of: Computer languages, systems & structures. Volume 54(2018)
• Page Start: 316
• Page End: 336
• Publication Date: 2018-12
• Subjects: Model-driven software development -- Domain-specific languages -- Data mining -- Data mining democratisation; Programming languages (Electronic computers) -- Periodicals; Computer networks -- Periodicals; Computer architecture -- Periodicals; Computer systems -- Periodicals; Langage de programmation; Réseau d'ordinateurs; Architecture d'ordinateur; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 005.13
• DOI: 10.1016/j.cl.2018.07.002
• Languages: English
• ISSNs: 1477-8424

Abstract

Highlights: A framework for the development of DSLs for data mining democratisation (DMDLs). Development costs of these DMDLs reduces by 50%. Development costs of DMDLs decrease by promoting reuse. Abstract: Companies have an increasing interest in employing data mining to take advantage of the vast amounts of data their systems store nowadays. This interest confronts two problems: (1) business experts usually lack the skills required to apply data mining techniques, and (2) the specialists who know how to use these techniques are a scarce and valuable asset. To help democratise data mining, we proposed, in a previous work, the development of domain-specific languages (DSLs) that hide the complexity of data mining techniques. The objective of these DSLs is to allow business experts to specify analysis processes by using high-level primitives and terminology from the application domain. These specifications would then be automatically transformed into a low-level, executable form. Although these DSLs might offer a promising solution to the aforementioned problems, their development from scratch requires a considerable effort and, consequently, they are costly. In order to make these languages affordable, we present FLANDM, an ecosystem devised for the rapid development of DSLs for data mining democratisation. FLANDM provides a base infrastructure that can be easily customised for the particularities of each domain, enabling controlled and systematic reuse of previously developed artefacts. By using FLANDM, new DSLs for data mining democratisation can be defined achieving a 50% of reduction in their development costs.