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

Document Type : Research Paper

Authors

1 PhD Candidate in IT, Faculty of Management, University of Tehran, Tehran, Iran

2 Associate Prof. in IT, Faculty of Management, University of Tehran, Tehran, Iran

3 Prof. of Industrial Engineering, University of Science and Industry, Tehran, Iran

4 Assistant Prof., Dep. of Information and Knowledge, University of Tehran, Tehran, Iran

Abstract

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.

Keywords

Main Subjects


Alexander, J., Chase, J., Newman, N., Porter, A. & Roessner, J. D. (2012). Emergence as a conceptual framework for understanding scientific and technological progress. Paper presented at the Technology Management for Emerging Technologies (PICMET), 2012 Proceedings of PICMET'12.
Blondel, V.D., Guillaume, J.L., Lambiotte, R. & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), 10008.
Boyack, K. W., Klavans, R., Small, H. & Ungar, L. (2014). Characterizing the emergence of two nanotechnology topics using a contemporaneous global micro-model of science. Journal of Engineering and Technology Management, 32, 147-159.
Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences, 101(suppl 1), 5303-5310.
Cozzens, S., Gatchair, S., Kang, J., Kim, K.-S., Lee, H. J., Ordóñez, G. & Porter, A. (2010). Emerging technologies: quantitative identification and measurement. Technology Analysis & Strategic Management, 22(3), 361-376.
Garfield, E. (1972). Citation analysis as a tool in journal evaluation  Science 178.4060 (1972): 471-479.
Guo, H., Weingart, S. & Börner, K. (2011). Mixed-indicators model for identifying emerging research areas. Scientometrics, 89(1), 421-435.
Kajikawa, Y., Yoshikawa, J., Takeda, Y. & Matsushima, K. (2008). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75(6), 771-782.
Kuhn, T. S. & Hawkins, D. (1963). The structure of scientific revolutions. American Journal of Physics, 31(7), 554-555.
Lee, W. H. (2008). How to identify emerging research fields using scientometrics: An example in the field of Information Security. Scientometrics, 76(3), 503-525.
Madlock-Brown, C. R. (2014). A framework for emerging topic detection in biomedicine. Diss. THE UNIVERSITY OF IOWA
Mecella, M., Scannapieco, M., Virgillito, A., Baldoni, R., Catarci, T. & Batini, C. (2002). Managing data quality in cooperative information systems. Paper presented at the OTM Confederated International Conferences, On the Move to Meaningful Internet Systems. CoopIS, DOA, and ODBASE. OTM 2002. Lecture Notes in Computer Science, vol 2519. Springer, Berlin, Heidelberg.
Price, D. J. (1970). Citation measures of hard science, soft science, technology, and nonscience. Communication among scientists and engineers, 3-22.
Sadiq, S., Yeganeh, N. K., & Indulska, M. (2011). 20 years of data quality research: themes, trends and synergies. Paper presented at the Proceedings of the Twenty-Second Australasian Database Conference, 115, 153-162.
Small, H., Boyack, K. W. & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450-1467.
Small, H. G. (1977). A co-citation model of a scientific specialty: A longitudinal study of collagen research. Social studies of science, 7(2), 139-166.
Tu, Y.-N. & Seng, J.-L. (2012). Indices of novelty for emerging topic detection. Information processing & management, 48(2), 303-325.
Wang, X., Cheng, Q. & Lu, W. (2014). Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks. Scientometrics, 101(2), 1253-1271.