Designing a Model Based on Cumulative Citation to Identify and Analyze Scientific Changes in the Field of Data Quality

Identification and tracking scientific changes is critical for scientific policy makers. This research proposed a model for identification and tracking changes in Data Quality research area. We used cumulative citation network in order to find research communities and their changes during the time. The proposed model can be applied in other scientific disciplines. It can also shows all types of scientific changes including birth, growth, merging and death. In order to verify the model in Data Quality area, we selected all papers that is published between 1970 and 2009 that covers more than 7000 papers. It is shown that Data Quality research area is studied in different disciplines. According to the results, there is 82 percent correlation between number of citations and the growth of Data Quality communities that shows the importance of citation for community survival and growth.