In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect lab...

Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and wrong business decisions. Data cleaning exercise often consist of two phases: error detection and error repairing. Error detection techniques can either be quantitative or qualitative; and error repairing is performed by applying data transformation script...

This article aims to understand the views of genomics scientists with regard to the data quality assurances associated with semiotics and Data-Information-Knowledge (DIK). The resulting communication of signs generated from genomic curation work, was found within different semantic levels of DIK that correlate specific data quality dimensions with their respective skills. Syntactic DQ dimension...

Innovation is vital to find new solutions to problems, increase quality, and improve profitability. Big open linked data (BOLD) is a fledgling and rapidly evolving field that creates new opportunities for innovation. However, none of the existing literature has yet considered the interrelationships between antecedents of innovation through BOLD. This research contributes to knowledge building t...

To succeed in their tasks, users need to manage data with the most adequate quality levels possible according to specific data quality models. Typically, data quality assessment consists of calculating a synthesizing value by means of a weighted average of values and weights associated with each data quality dimension of the data quality model. We shall study not only the overall perception of ...

This paper, based on 28 interviews from a range of business leaders and practitioners, examines the current state of big data use in business, as well as the main opportunities and challenges presented by big data. It begins with an account of the current landscape and what is meant by big data. Next, it draws distinctions between the ways organisations use data and provides a taxonomy of big d...

Poor data quality is a serious and costly problem affecting organizations across all industries. Real data is often dirty, containing missing, erroneous, incomplete, and duplicate values. Declarative data cleaning techniques have been proposed to resolve some of these underlying errors by identifying the inconsistencies and proposing updates to the data. However, much of this work has focused o...