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2017 | OriginalPaper | Chapter

A Software Architecture for Enabling Statistical Learning on Big Data

Authors : Ali Behnaz, Fethi Rabhi, Maurice Peat

Published in: Advances in Time Series Analysis and Forecasting

Publisher: Springer International Publishing

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Abstract

Most big data analytics research is scattered across multiple disciplines such as applied statistics, machine learning, language technology or databases. Little attention has been paid to aligning big data solutions with end-user’s mental models for conducting exploratory and predictive data analysis. We are particularly interested in the way domain experts perform big data analysis by applying statistics to big data with a focus on statistical learning. In this paper we compare and contrast the different views about data between the fields of statistics and computer science. We review popular analysis techniques and tools within a defined analytics stack. We then propose a model-driven architecture that uses semantic and event processing technologies to achieve a separation of concerns between expressing the mathematical model and the computational requirements. The paper also describes an implementation of the proposed architecture with a case study in funds management.

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Footnotes
1
This is a multiple linear regression, a widely used form in statistical learning.
 
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Metadata
Title
A Software Architecture for Enabling Statistical Learning on Big Data
Authors
Ali Behnaz
Fethi Rabhi
Maurice Peat
Copyright Year
2017
Book
Advances in Time Series Analysis and Forecasting

Print ISBN: 978-3-319-55788-5

Electronic ISBN: 978-3-319-55789-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-55789-2_24