Goal-based composition of scalable hybrid analytics for heterogeneous architectures

Crafting scalable analytics in order to extract actionable business intelligence is a challenging endeavour, requiring multiple layers of expertise and experience. Often, this expertise is irreconcilably split between an organisation’s engineers and subjectmatter domain experts. Previous approaches to this problemhave relied on technically adept users with tool-specific training. Such an approach has a number of challenges: Expertise— There are few data-analytic subject domain experts with in-depth technical knowledge of compute architectures; Performance — Analysts do not generally make full use of the performance and scalability capabilities of the underlying architectures; Heterogeneity — calculating the most performant and scalable mix of real-time (on-line) and batch (offline) analytics in a problem domain is difficult; Tools — Supporting frameworks will often direct several tasks, including, composition, planning, code generation, validation, performance tuning and analysis, but do not typically provide end-to-end solutions embedding all of these activities. In this paper, we present a novel semi-automated approach to the composition, planning, code generation and performance tuning of scalable hybrid analytics, using a semantically rich type system which requires little programming expertise from the user. This approach is the first of its kind to permit domain expertswith little or no technical expertise to assemble complex and scalable analytics, for hybrid onand off-line analytic environments,with no additional requirement for low-level engineering support. This paper describes (i) an abstract model of analytic assembly and execution, (ii) goal-based planning and (iii) code generation for hybrid onand off-line analytics. An implementation, through a systemwhich we call Mendeleev, is used to (iv) demonstrate the applicability of this technique through a series of case studies, where a single interface is used to create analytics that can be run simultaneously over onand off-line environments. Finally, we (v) analyse the performance of the planner, and (vi) show that the performance ofMendeleev’s generated code is comparable with that of hand-written analytics. © 2016 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). ∗ Corresponding author. E-mail addresses: [email protected] (P. Coetzee), [email protected] (S.A. Jarvis).