Abstract
Symbolic Data Analysis is an extension of Classical Data Analysis to more complex data types and tables through the application of certain conditions, where underlying concepts are vital for their further processing. Therefore, the assessment of the quality of Symbolic Data depends extensively on the quality of the collected classical data. However, even though various criteria and indicators have been established to assess quality in classsical statistics, the specificities of Symbolic Data construction challenge the efficacy of the classical quality assessment components. In this paper we initially refer to the quality dimensions that can be considered for the classical data and then emphasize on the extent that these can be applied to symbolic data, taking into account the peculiarities of symbolic approach.
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References
BILLARD, L. and DIDAY, E. (2003): From the Statistics of Data to the Statistics of knowledge: Symbolic Data Analysis. Journal of the American Statistical Association (JASA), 98(462), 470–487.
BOCK, H.-H. and DIDAY, E. (2000): Analysis of Symbolic Data, Springer-Verlang, Berlin.
DIDAY, E. (2000): Symbolic Data Analysis and the SODAS project: Purpose, History, Perspective. In: H.-H. Bock and E. Diday (Eds.): Analysis of Symbolic Data, Springer-Verlang, Berlin, 1–22.
DIDAY, E. (2002): An introduction to Symbolic Data Analysis and the Sodas software. The Electronic Journal of SYMBOLIC Data Analysis (JSDA), 0(0), 1–25.
EUROSTAT (2002a): Definition of quality in statistics. Retrieved from http://forum.europa.eu.int/Public/irc/dsis/Home/main.
EUROSTAT (2002b): Standard quality report. Retrieved from http://forum.europa.eu.int/Public/irc/dsis/Home/main.
IMF (2002): Data Quality Assessment Framework and Data Quality Program. Retrieved from http://www.imf.org/external/np/sta/dsbb/2003/eng/dqaf.htm
LINDEN, H. & PAPAGEORGIOU, H. (2004): Standard Quality Indicators. European Conference on Quality and Methodology in Official Statistics (Q2004), Mainz, Germany. Also to appear in Statistical Research Reference Material, Japan Statistical Research Institute, Hosei University, Tokyo, Japan.
NOIRHOMME-FRAITURE, M. (1997): Zoom-Star, a solution to complex statistical objects representation. In: St. Howard, J. Hammond and G. Lindgaard (Eds.): Proc. INTERACT’ 97, Sydney, Australia.
OECD (2003): Quality framework and guidelines for OECD statistical activities. Retrieved from http://www.oecd.org/dataoecd/26/42/21688835.pdf
OFFICE OF MANAGEMENT AND BUDGET (OMB) (2002): Information Quality Guidelines. Retrieved from http://www.whitehouse.gov/omb/inforeg/iqg_oct2002.pdf
PAPAGEORGIOU, H., VARDAKI, M. and PENTARIS, F. (2000): Data and Metadata Transformations. Research in Official Statistics (ROS), 3(2), 27–43.
PAPAGEORGIOU, H., PENTARIS, F., THEODOROU, E., VARDAKI, M. and PETRAKOS, M. (2001): A statistical metadata model for simultaneous manipulation of data and metadata. Journal of Intelligent Information Systems (JIIS), 17(2/3), 169–192.
PAPAGEORGIOU, H., VARDAKI. M., THEODOROU, E. and PENTARIS, F. (2002): The use of Statistical Metadata Modelling and related transformations to assess the quality of statistical reports. Joint UNECE/Eurostat Seminar on Integrated Statistical Information Systems and Related Matters (ISIS 2002), Geneva, Switzerland.
PAPAGEORGIOU, H. and VARDAKI, M. (2006): A Statistical Metadata Model for Symbolic Objects, to appear in the forthcoming E.Diday & M.Noirhomme (Eds.): Symbolic Data Analysis and the SODAS Software, Wiley.
STATISTICS CANADA (2003): Quality guidelines. Fourth Edition. Statistics Canada, Ottawa. Retrieved from http://www.statcan.ca/english/freepub/12-539-XIE/12-539XIE03001.pdf
STATISTICS FINLAND (2002): Quality guidelines for Official Statistics. Statistics Finland, Handbooks 43b. Helsinki, Finland.
VARDAKI, M. (2005a): Metadata for Symbolic Objects. Electronic Journal of Symbolic Data Analysis (JSDA), 2(1), 1–8.
VARDAKI, M. (2005b): Statistical Metadata in Data Processing and Interchange. In J. Wang (Ed): Encyclopedia of Data Warehousing and Mining, IDEA Group publishing, 2, 1048–1053.
VARDAKI, M. and PAPAGEORGIOU, H. (2004): An integrated metadata model for statistical data collection and processing. Proc. of the Sixteenth International Conference on Scientific and Statistical Database Management (SSDBM), Santorini, Greece, 363–372.
VARDAKI, M. and PAPAGEORGIOU, H. (2006): Statistical Data and Metadata Quality Assessment. To appear in the forthcoming M. Khosrow-Pour (Ed,): Encyclopedia of Public Information Technologies, IDEA Group Publishing, USA.
VIGGO, S.H., BYFUGLIEN, J. and JOHANNESSEN, R. (2003): Quality Issues at Statistics Norway. Journal of Official Statistics (JOS), 19(3), 287–303.
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Papageorgiou, H., Vardaki, M. (2007). Quality Issues in Symbolic Data Analysis. In: Brito, P., Cucumel, G., Bertrand, P., de Carvalho, F. (eds) Selected Contributions in Data Analysis and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73560-1_11
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DOI: https://doi.org/10.1007/978-3-540-73560-1_11
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