2015/03/17 by Jeremy Debattista, Santiago Londoño, Debattista, Jeremy +5
Computer Science · Decision Sciences · #Data Mining Algorithms and Applications #Data Quality and Management #Data Structures and Algorithms (cs.DS) #Databases (cs.DB) #FOS: Computer and information sciences #Semantic Web and Ontologies #cs.DB #cs.DS
paper · pdf · doi:10.48550/arxiv.1503.05157
15 pages, 2 figures, To appear in ESWC 2015 proceedings
arxiv created 2015/03/17 · openalex publication_date 2015/03/17 · arxiv updated 2015/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact, deterministic computation of the quality metrics is too time consuming or expensive. We employ probabilistic techniques such as Reservoir Sampling, Bloom Filters and Clustering Coefficient estimation for implementing a broad set of data quality metrics in an approximate but sufficiently accurate way. Our implementation is integrated in the comprehensive data quality assessment framework Luzzu. We evaluated its performance and accuracy on Linked Open Datasets of broad relevance.