Editorial: Special Issue on Quality Assessment and Management in Big Data—Part I

Abstract

It is a pleasure for us to introduce this Special Issue on Quality Assessment and Management in Big Data, Part I—Journal of Data and Information Quality, ACM. We have received 27 original submissions from which 11 final papers have been selected for publication (after a rigorous peer review process) in this issue divided into two parts. This editorial corresponds to Part I, in which we included papers related to machine learning and quality management in big data scenarios. In the era of big data [1], organizations are dealing with tremendous amounts of data, which are fast-moving and can originate from various sources, such as social networks [2], unstructured data from various websites [3], or raw feeds from sensors [4]. Big data solutions are used to optimize business processes and reduce decision-making times, so as to improve operational effectiveness. Big data practitioners are experiencing a huge number of data quality problems [5]. These can be time-consuming to solve or even lead to incorrect data analytics. How to manage quality in big data has become challenging, and thus far research has only addressed limited aspects. Given the complex nature of big data, traditional data quality management approaches cannot simply be applied to big data quality management.