Goal-based composition of scalable hybrid analytics for heterogeneous architectures

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

External organisations

  • University of Warwick

Abstract

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 subject matter domain experts. Previous approaches to this problem have 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 (off-line) 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 experts with little or no technical expertise to assemble complex and scalable analytics, for hybrid on- and 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 on- and off-line analytics. An implementation, through a system which 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 on- and off-line environments. Finally, we (v) analyse the performance of the planner, and (vi) show that the performance of MENDELEEV's generated code is comparable with that of hand-written analytics.

Bibliographic note

Funding Information: This research was funded by a UK Engineering and Physical Sciences Research Council (EPSRC) (EP/K503204/1) Industrial CASE Studentship, entitled ?Platforms for Deploying Scalable Parallel Analytic Jobs over High Frequency Data Streams?. Compute platforms are provided through an EPSRC Capital Equipment Grant, ?Provision of a Portfolio of Massively Parallel, Data-intensive Analytics Platforms?. The Alan Turing Institute is a joint venture between the universities of Cambridge, Edinburgh, Oxford, Warwick, University College London and the Engineering and Physical Sciences Research Council. The Institute will promote the development and use of advanced mathematics, computer science, algorithms and big data for human benefit. Publisher Copyright: © 2016 The Author(s)

Details

Original languageEnglish
Pages (from-to)59-73
Number of pages15
JournalJournal of Parallel and Distributed Computing
Volume108
Publication statusPublished - Oct 2017

Keywords

  • Analytic planning, Data intensive computing, Data science, Hadoop, Heterogeneous compute, Hybrid analytics, Streaming analysis