Data Quality Assessment

By L. Pipino, Yang W. Lee, Richard Y. Wang. 2002

Subfields: Evaluation & Benchmarks, Software Engineering

Citations in the corpus

• Measuring Data (2022, neutral): “, 2015); “task-independent metrics” to reflect states of the data without the contextual knowledge of the application (Pipino et al., 2002); “characteristic metrics”, which the authors define as “unsupervised measures” to quantitatively describe the properties of a collection of data or a dataset (L”
• Effects of tourism information quality in social media on destination image formation: The case of Sina Weibo (2017, unknown): “For example, Pipino et al. (2002) reorganized this conceptual framework into subjective and objective dimensions, and Kahn et al. (2002) used this framework to map IQ dimensions into Product-Service-Performance/IQ (PSP/IQ) model and assessed soundness, usefulness, dependability, and usability as…”
• Creating Strategic Business Value from Big Data Analytics: A Research Framework (2018, unknown): “, [42]), the key question of data value stems from whether BDA can provide novel and valuable insights to exploit new business opportunities or defend competition threats.”
• When owl: sameAs Isn't the Same: An Analysis of Identity in Linked Data (2010, unknown): “While there has been much related work in the database community on assessing information quality from uncertain sources of information [16], and some work in the Semantic Web community such as the work of WIQA [2] and Inference Web [13], this work has yet to be widely deployed for Linked Data.”
• Quality assessment for Linked Data: A Survey (2015, unknown): “In particular, the database community has developed a number of approaches [4,38,51,62].”
• Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, unknown): “The effectiveness of FNN training primarily depends on data quality, which is governed by the following data quality assurance parameters: accuracy, reliability, timeliness, relevance, completeness, currency, consistency, flexibility, and precision [333, 334].”
• A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, unknown): “While this is a seemingly simple question, it is actually extremely challenging to define data quality without knowing the ground truth [28, 7, 10, 29, 11, 19].”
• Assessing data quality in citizen science (preprint) (2016, unknown): “The concept of data quality is multi- dimensional, consisting of more than a dozen possible nonexclusive metrics (Pipino et al. 2002).”
• Big Data Quality: A Quality Dimensions Evaluation (2016, unknown): “In [1], [4], [6], [7] Data quality assessment was discussed early in literature as in [8] where they divide data quality assessment into two main categories: subjective and objective.”
• Machine Learning for Synthetic Data Generation: a Review (2023, unknown): “When data is of subpar quality, models may generate incorrect or imprecise predictions due to confusion and misinterpretation [6] [7].”
• A Survey on Data Quality: Classifying Poor Data (2015, unknown): “Previous research and industry reports are clear in indicating the severe damage caused by the presence of poor quality data in diverse contexts and at many different levels [12], [4], [13], [14].”
• Antecedents of Information and System Quality: An Empirical Examination Within the Context of Data Warehousing (2005, unknown): “From this perspective, information quality is assessed by the degree to which it is helpful in completing a particular task [27, 45, 62, 72, 74, 81, 82].”
• Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research (2013, unknown): “In addition to the overreliance on gold standards, the majority of the studies we identi ed relied upon an ‘ intuitive ’ understanding fi 107 109 methodologies that were developed with a minimal empirical or theoretical basis.”
• Knowledge graph refinement: A survey of approaches and evaluation methods (2016, unknown): “From a data quality perspective, those goals relate to the data quality dimensions free-of-error and completeness [84].”
• Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO (2017, unknown): “introduce the criteria schema completeness, column completeness and population completeness in the context of databases [35].”
• Profiling relational data: a survey (2015, unknown): “In [104], a book addressing practitioners, several of these tasks are discussed in more detail.”
• Data quality: A survey of data quality dimensions (2012, unknown): “Ease of Manipulation To extend to which data is easy to manipulate and apply to different same format [22].”