Related papers: Framework to Automatically Determine the Quality o…
Recent advances in generating synthetic data that allow to add principled ways of protecting privacy -- such as Differential Privacy -- are a crucial step in sharing statistical information in a privacy preserving way. But while the focus…
In recent past, big data opportunities have gained much momentum to enhance knowledge management in organizations. However, big data due to its various properties like high volume, variety, and velocity can no longer be effectively stored…
Nowadays, the use of synthetic data has gained popularity as a cost-efficient strategy for enhancing data augmentation for improving machine learning models performance as well as addressing concerns related to sensitive data privacy.…
With the increasing adoption and growth of the Linked Open Data cloud [9], with RDFa, Microformats and other ways of embedding data into ordinary Web pages, and with initiatives such as schema.org, the Web is currently being complemented…
In recent years, the role and the importance of software in the automotive domain have changed dramatically. Being able to systematically evaluate and manage software quality is becoming even more crucial. In practice, however, we still…
Good software documentation encourages good software engineering, but the meaning of "good" documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information…
This report discusses the issues of data quality in biobanks. It presents the state-of-the-art in data quality: the definition of data quality, the dimensions of data quality, and the quality management system for achieving or describing…
Over the years, Software Quality Engineering has increased interest, demonstrated by significant research papers published in this area. Determining when a software artifact is qualitatively valid is tricky, given the impossibility of…
Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce,…
In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data sources provided by the advertisers or third-party data…
Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collaborative settings where sharing is not allowed directly between…
In the distributed and dynamic framework of the Web, data quality is a big challenge. The Linked Open Data (LOD) provides an enormous amount of data, the quality of which is difficult to control. Quality is intrinsically a matter of usage,…
Data quality and data cleaning are context dependent activities. Starting from this observation, in previous work a context model for the assessment of the quality of a database instance was proposed. In that framework, the context takes…
Improving data quality in unstructured documents is a long-standing challenge. Unstructured data, especially in textual form, inherently lacks defined semantics, which poses significant challenges for effective processing and for ensuring…
High data quality is critical for reliable analytics and operational efficiency. A growing ecosystem of tools has emerged to support data quality management, ranging from lightweight open-source libraries to comprehensive enterprise…
Artificial intelligence has transformed numerous industries, from healthcare to finance, enhancing decision-making through automated systems. However, the reliability of these systems is mainly dependent on the quality of the underlying…
Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process…
Context - The exponential growth of data is becoming a significant concern. Managing this data has become incredibly challenging, especially when dealing with various sources in different formats and speeds. Moreover, Ensuring data quality…
While high data quality (DQ) is critical for analytics, compliance, and AI performance, data quality management (DQM) remains a complex, resource-intensive, and often manual process. This study investigates the extent to which existing…
Data is one of the most important assets of the information age, and its societal impact is undisputed. Yet, rigorous methods of assessing the quality of data are lacking. In this paper, we propose a formal definition for the quality of a…