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(to be completed)

 


ThinkMind // International Journal On Advances in Software, volume 10, numbers 1 and 2, 2017 // View article soft_v10_n12_2017_1


Data Quality Considerations for Big Data and Machine Learning: Going Beyond Data Cleaning and Transformations

Authors:
Venkat Gudivada
Amy Apon
Junhua Ding

Keywords: Data Quality; Data Quality Assessment; Data Cleaning; Big Data; Machine Learning; Data Transformation

Abstract:
Data quality issues trace back their origin to the early days of computing. A wide range of domain-specific techniques to assess and improve the quality of data exist in the literature. These solutions primarily target data which resides in relational databases and data warehouses. The recent emergence of big data analytics and renaissance in machine learning necessitates evaluating the suitability relational database-centric approaches to data quality. In this paper, we describe the nature of the data quality issues in the context of big data and machine learning. We discuss facets of data quality, present a data governance-driven framework for data quality lifecycle for this new scenario, and describe an approach to its implementation. A sampling of the tools available for data quality management are indicated and future trends are discussed.

Pages: 1 to 20

Copyright: Copyright (c) to authors, 2017. Used with permission.

Publication date: June 30, 2017

Published in: journal

ISSN: 1942-2628

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