Systematic method for finding emergence research areas as data quality.

Abstract The analysis of the transformation and changes in scientific disciplines has always been a critical path for policymakers and researchers. The current study examines the changes in the research areas of data and information quality (DIQ). The aim of this study was to detect different types of changes occurring in the scientific areas including birth, death, growth, decline, merge, and splitting. A model has been developed for this data mining. To test the model, all DIQ articles published in online scientific citation indexing service or Web of Science (WOS) between 1970 and 2016 were extracted and analyzed using the given model. The study is related to the Big Data as well as the integration methods in Big Data which is the most important area in DIQ. It is demonstrated that the first and second emerging research areas are sub-disciplines of entity resolution and record linkage. Accordingly, linkage and privacy are the first emerging research area and the entity resolution using ontology is the second in DIQ. This is followed by the social media issues and genetic related DIQ issues. Highlights • Developing novel approach for finding scientific threads • Developing a model to show behavior of scientific research areas • Developing a model for finding top research areas of scientific disciplines • Finding DIQ emerging research areas [ABSTRACT FROM AUTHOR]