相关论文: Open Data and the Status Quo -- A Fine-Grained Eva…

The research discusses how (open) data quality could be described, what should be considered developing a data quality management solution and how it could be applied to open data to check its quality. The proposed approach focuses on…

Data catalogs play a crucial role in modern data-driven organizations by facilitating the discovery, understanding, and utilization of diverse data assets. However, ensuring their quality and reliability is complex, especially in open and…

Data warehousing is continuously gaining importance as organizations are realizing the benefits of decision oriented data bases. However, the stumbling block to this rapid development is data quality issues at various stages of data…

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open…

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…

Metadata are critical in epidemiological and public health research. However, a lack of biomedical metadata quality frameworks and limited awareness of the implications of poor quality metadata renders data analyses problematic. In this…

Nowadays open data is entering the mainstream - it is free available for every stakeholder and is often used in business decision-making. It is important to be sure data is trustable and error-free as its quality problems can lead to huge…

The term Data Space, understood as the secure exchange of data in distributed systems, ensuring openness, transparency, decentralization, sovereignty, and interoperability of information, has gained importance during the last years.…

High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for…

Data quality describes the degree to which data meet specific requirements and are fit for use by humans and/or downstream tasks (e.g., artificial intelligence). Data quality can be assessed across multiple high-level concepts called…

The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A…

One of the most significant problems of Big Data is to extract knowledge through the huge amount of data. The usefulness of the extracted information depends strongly on data quality. In addition to the importance, data quality has recently…

The widespread adoption of big data has ushered in a new era of data-driven decision-making, transforming numerous industries and sectors. However, the efficacy of these decisions hinges on the quality of the underlying data. Poor data…

Data-oriented applications, their users, and even the law require data of high quality. Research has divided the rather vague notion of data quality into various dimensions, such as accuracy, consistency, and reputation. To achieve the goal…

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…

Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still…

Increasing amounts of structured data can provide value for research and business if the relevant data can be located. Often the data is in a data lake without a consistent schema, making locating useful data challenging. Table search is a…

Data quality is a key element for building and optimizing good learning models. Despite many attempts to characterize data quality, there is still a need for rigorous formalization and an efficient measure of the quality from available…

Assessing and improving the quality of data are fundamental challenges for data-intensive systems that have given rise to applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data…

Data quality is commonly defined as fitness for use. The problem of identifying quality of data is faced by many data consumers. Data publishers often do not have the means to identify quality problems in their data. To make the task for…