FSS++ Workshop Report: Handling Uncertainty for Data Quality Management

This report describes the results of the eSCF Awareness Workshop on Handling Uncertainty for Data Quality Management - Challenges from Transport and Supply Chain Management that was held on June 5, 2018 in Heeze, The Netherlands. The goal of this workshop was to create and enhance awareness into data quality management issues that are encountered in practice, for business organizations that aim to integrate a data-analytical mind set into their operations

Toward a framework for data quality in cloud-based health information system

This Cloud computing is a promising platform for health information systems in order to reduce costs and improve accessibility. Cloud computing represents a shift away from computing being purchased as a product to be a service delivered over the Internet to customers. Cloud computing paradigm is becoming one of the popular IT infrastructures for facilitating Electronic Health Record (EHR) integration and sharing. EHR is defined as a repository of patient data in digital form. This record is stored and exchanged securely and accessible by different levels of authorized users. Its key purpose is to support the continuity of care, and allow the exchange and integration of medical information for a patient. However, this would not be achieved without ensuring the quality of data populated in the healthcare clouds as the data quality can have a great impact on the overall effectiveness of any system. The assurance of the quality of data used in healthcare systems is a pressing need to help the continuity and quality of care. Identification of data quality dimensions in healthcare clouds is a challenging issue as data quality of cloud-based health information systems arise some issues such as the appropriateness of use, and provenance. Some research proposed frameworks of the data quality dimensions without taking into consideration the nature of cloud-based healthcare systems. In this paper, we proposed an initial framework that fits the data quality attributes. This framework reflects the main elements of the cloud-based healthcare systems and the functionality of EHR

Visible Evidence of Invisible Quality Dimensions and the Role of Data Management

P ast research ha s shown that data reusers are concerned with the issue of data quality and the identified attributes of quality. While data reusers find evidence of the attributes of data quality during their assessment of data for reuse, there may be other dimensions of data quality that reusers are concern ed about but that are not always visible to them. This study explores these invisible dimensions of data quality that have been identified by data reusers. The findings of this study indicate that data reusers are concern ed with two kinds of invisible characteristics for assessing the data : the efforts put on data , and the ethics behind the data. While these quality dimensions cannot be easily measured at face - level, data reusers find proxy evidence that indicate s the presence of these invisibilities. This finding signifies the role of data management that can make these invisible data qualit ies visibl

AI in the newsroom: A data quality assessment framework for employing machine learning in journalistic workflows

[EN] AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI-driven systems accountable, less attention is paid to data quality, while the results' accuracy and efficiency depend on high-quality data. However, data quality remains complex to define insofar as it is a multidimensional, highly domain-dependent concept. Assessing data quality in the context of AI-driven journalism requires a broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. These considerations ground a conceptual data quality assessment framework that aims to support the collection and pre-processing stages in machine learning. It aims to contribute to strengthening data literacy in journalism and to make a bridge between scientific disciplines that should be viewed through the lenses of their complementarity.Dierickx, L.; Lindén, C.; Opdahl, A.; Khan, S.; Guerrero Rojas, D. (2023). AI in the newsroom: A data quality assessment framework for employing machine learning in journalistic workflows. Editorial Universitat Politècnica de València. 217-225. https://doi.org/10.4995/CARMA2023.2023.1644021722

Measuring Data Completeness for Microbial Genomics Database

Poor quality data such as data with missing values (or records)cause negative consequences in many application domains. An important aspect of data quality is completeness. One problem in data completeness is the problem of missing individuals in data sets. Within a data set, the individuals refer to the real world entities whose information is recorded. So far, in completeness studies however, there has been little discussion about how missing individuals are assessed. In this paper, we propose the notion of population-based completeness (PBC) that deals with the missing individuals problem, with the aim of investigating what is required to measure PBC and to identify what is needed to supportPBC measurements in practice. This paper explores the need of PBC in the microbial genomics where real sample data sets retrieved from a microbial database called Comprehensive Microbial Resources are used(CMR)