Iranian Journal of Information Management
Analyzing the Evolution of Data Quality Research Area Using Co-word Analysis
Document Type : Original Article
Author
PhD. IT Management, Faculty of Management, University of Tehran, Tehran, Iran
Abstract
The domain of data quality is one of the growing and important areas in the field of information systems. The exact recognition of this field on the one hand and the recognition of the features of the new sub-fields of this field and its interdisciplinary of it will be of great importance to researchers. This knowledge is important for them to decide on the research process and the choice of the field of activity. For this purpose, in this study, using the graph of keyword co-occurrence is conducted on more than 9000 papers. Based on this study, it has been found that these domains have been more interdisciplinary and focus on the relationship between several fields of study. In other words, these keywords are more closely associated with the keywords in the other clusters than with the keywords they are in the same cluster. According to this study, the latest are is big data that focused on integration issues and missing data in this area.
Keywords
Batini, C. and M. Scannapieca. 2006. "Introduction to Data Quality." Data Quality: Concepts, Methodologies and Techniques: 1-18.
Batini, C. and M. Scannapieco. 2006. "Data Quality Concepts, Methodologies and Techniques" Springer-Verlag.
Blake, R. 2010. "Identifying the core topics and themes of data and information quality research". AMCIS.
Blake, R. and G. Shankaranarayanan. 2012. "Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis." International Journal of Business Intelligence Research (IJBIR) 3(1): 1-16.
Blondel, V. D., J.-L. Guillaume, R. Lambiotte and E. Lefebvre. 2008. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008(10): P10008.
Brodie, M. L. 1980. "Data quality in information systems." Information & Management 3(6): 245-258.
Chaffey, D. and G. White. 2010. "Business information management: improving performance using information systems." Pearson Education.
Du, J. and L. Zhou. 2012. "Improving financial data quality using ontologies." Decision Support Systems 54(1): 76-86.
Gschwandtner, T., J. Gärtner, W. Aigner and S. Miksch. 2012. "A taxonomy of dirty time-oriented data." Multidisciplinary Research and Practice for Information Systems, Springer: 58-72.
Helfert, M. and M. Ge. 2006. "A review of information quality research." 11th International Conference on Information Quality.
Khalilijafarabad, A., M. Helfert and M. Ge. 2016. "Developing a Data Quality Research Taxonomy–an Organizational Perspective." International Conference on Information and Data Quality, Spain.
Kim, W., B.-J. Choi, E.-K. Hong, S.-K. Kim and D. Lee. 2003. "A taxonomy of dirty data." Data mining and knowledge discovery 7(1): 81-99.
Kim, Y. J., R. Kishore and G. L. Sanders. 2005. "From DQ to EQ: understanding data quality in the context of e-business systems." Communications of the ACM 48(10): 75-81.
Lima, L. F. R., A. C. G. Maçada and L. M. Vargas. 2006. "Research into Information Quality: A Study of the State of the Art in IQ and Its Consolidation". International Conference on Information Quality.
Madnick, S. E., R. Y. Wang, Y. W. Lee and H. Zhu. 2009. "Overview and framework for data and information quality research." Journal of Data and Information Quality (JDIQ) 1(1): 2.
Morris, S. A. and B. Van der Veer Martens. 2008. "Mapping research specialties." Annual review of information science and technology 42(1): 213-295.
Neely, M. P. and J. Cook. 2008. "A Framework for Classification of the Data and Information Quality Literature and Preliminart Results (1996-2007)." AMCIS 2008 Proceedings: 131.
Oliveira, P., F. Rodrigues, P. Henriques and H. Galhardas. 2005. "A taxonomy of data quality problems." 2nd Int. Workshop on Data and Information Quality, Citeseer.
Oliveira, P., F. Rodrigues and P. R. Henriques. 2005. "A Formal Definition of Data Quality Problems." IQ.
Porter, A. L., D. J. Roessner and A. E. Heberger. 2008. "How interdisciplinary is a given body of research?" Research evaluation 17(4): 273-282.
Qi, W. 2016. "Studies in the Dynamics of Science: Exploring emergence, classification, and interdisciplinarity". KTH Royal Institute of Technology.
Rafols, I. and M. Meyer. 2009. "Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience." Scientometrics 82(2): 263-287.
Rafols, I., A. L. Porter and L. Leydesdorff . 2010. "Science overlay maps: A new tool for research policy and library management." Journal of the American Society for information Science and Technology 61(9): 1871-1887.
Rahm, E. and H. H. Do. 2000. "Data cleaning: Problems and current approaches." IEEE Data Eng. Bull. 23(4): 3-13.
Sadiq, S. 2013. "Prologue: Research and Practice in Data Quality Management." Handbook of Data Quality, Springer: 1-11.
Sadiq, S. W., N. K. Yeganeh and M. Indulska. 2011. "Cross-disciplinary collaborations in data quality research." European Conference on Information Systems.
Shankaranarayanan, G. and R. Blake. 2017. "From Content to Context: The Evolution and Growth of Data Quality Research." Journal of Data and Information Quality (JDIQ) 8(2): 9.
Simmhan, Y. L., B. Plale and D. Gannon. 2005. "A survey of data provenance in e-science." ACM Sigmod Record 34(3): 31-36.
Wang, R. Y. and D. M. Strong. 1996. "Beyond accuracy: What data quality means to data consumers." Journal of management information systems 12(4): 5-33.
Zhang, T., Y. Wu, H. Zhang, Y. Liu and W. Huang. 2013. "Identifying Data Quality/Information Quality Research: Framework and Evolution. Diversity, Technology, and Innovation for Operational Competitiveness". Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management, ToKnowPress.
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Volume 3, Issue 2 - Serial Number 5
March 2018Pages 121-138
- Receive Date: 28 September 2017
- Revise Date: 13 November 2017
- Accept Date: 19 February 2018