User-Driven Filtering and Ranking of Topical Datasets Based on Overall Data Quality

Abstract

Finding relevant and high-quality data is the eternal needs for data consumers (i.e., users). Many open data portals have been providing users with simple ways of finding datasets on a particular topic (i.e., topical datasets), which are not a way of filtering and ranking topical datasets based on data quality. Despite the recent advances in the development and standardization of data quality models and vocabulary, there is a lack of systematic research on approaches and tools for user-driven data quality-based filtering and ranking of topical datasets. In this paper we address the problem of user-driven filtering and ranking of topical datasets based on the overall data quality of datasets by developing a generic software architecture and the corresponding approach, called ODQFiRD, for filtering and ranking topical datasets according to user-specified data quality assessment criteria. Additionally, we use our implemented prototype of ODQFiRD to conduct a case study experiment on the U.S. Government's open data portal. The prototype implementation and experimental results show that our proposed ODQFiRD is achievable and effective.