Data quality and due process in large interorganizational record systems
Communications of the ACM
aHUGIN: a system creating adaptive causal probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Assessing data quality in accounting information systems
Communications of the ACM
Enhancing data quality in data warehouse environments
Communications of the ACM
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
AIMQ: a methodology for information quality assessment
Information and Management
Learning Bayesian Networks
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Typing Biometrics: Impact of Human Learning on Performance Quality
Journal of Data and Information Quality (JDIQ)
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
A multidimensional analysis of data quality for credit risk management: New insights and challenges
Information and Management
| Hi-index | 0.00 |
This research develops a data quality algorithm entitled the Accuracy Assessment Algorithm (AAA). This is an extension of research in developing an enhancement to a Bayesian Network (BN) learning algorithm called the Data Quality (DQ) algorithm. This new algorithm is concerned with estimating the accuracy levels of a dataset by assessing the quality of the data with no prior knowledge of the dataset. The AAA and associated metrics were tested using two canonical BNs and one large-scale medical network. The article presents the results regarding the efficacy of the algorithm and the implications for future research and practice.