A Sketch of Statistical Meta-Computing as a Data Integration Framework

Karl A. Froeschl

doi:10.17713/ajs.v33i1&2.437

Authors

Karl A. Froeschl ec3 Electronic Commerce Competence Center, Vienna

DOI:

https://doi.org/10.17713/ajs.v33i1&2.437

Abstract

Statistics defines itself as a methodological discipline providing a rigorous, formal framework for scientific empirism based on a mapping of contingent observable phenomena to (real) numbers that can be dealt with, or analysed, computationally. Application of the statistical methodology of data reduction, in turn, requires some representation of the problem context. Most of the time, this amounts to encoding (a part of) the problem context of observation data into another layer of data – called metadata. Based on metadata, procedures of data analysis might be enhanced to encompass also
the analysis and transformation of metadata alongside the accompanied data itself. The paper sketches the outline of a systematic approach to statistical “meta-computing” as a dual-mode proposal of statistical data processing.

References

S. Abiteboul, P. Buneman, D. Suciu. Data on the Web: From Relations to Semistructured Data and XML. Morgan Kaufmann, San Francisco et al., 2000.

J.G. Bethlehem, J.-P. Kent, Ad Willeboordse, W. Ypma. On the Use of Metadata in Statistical Data Processing. UN/ECE Work Session on Statistical Metadata Report, Working Paper No.23, Geneva, September 22-24, 1999, 11pp., 1999.

G.E.P. Box. Science and Statistics. JASA 71: 791-799 (Applications Section), 1976.

P. Darius, M. Boucneau, P. de Greef, E. de Feber, K. Froeschl. Modelling Metadata. Statistical Journal of the United Nations Economic Commission for Europe 10(2): 171–180, 1993.

M. Denk. Metadata Driven Production of Statistical Aggregates. Diploma Thesis, Dept. of Statistics and Decision Support Systems, University of Vienna, 1999.

M. Denk. Statistical Data Combination: A Metadata Framework for Record Linkage Procedures. Doctoral Thesis, Dept. of Statistics and Decision Support Systems, University of Vienna, 2002.

M. Denk and K.A. Froeschl. The IDARESA Data Mediation Architecture for Statistical Aggregates. Research in Official Statistics 3(1): 7–38, 2000.

M. Denk, K.A. Froeschl, W. Grossmann. Statistical Composites: A Transformationbound Representation of Statistical Datasets. In J. Kennedy (editor). Proc. 14th Int. Conf. Scientific and Statistical Database Management (Edinburgh, UK), pages 217- 226. IEEE Computer Society Press, Los Alamitos, Ca., 2002.

U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, R. Uthurusamy (editors). Advances in Knowledge Discovery and Data Mining. AAAI Press / The MIT Press, Menlo Park, Ca. et al., 1996.

K.A. Froeschl. Metadata Management in Statistical Information Processing. Wien-Berlin: Springer, 1997.

K.A. Froeschl, T. Yamada, R. Kudrna. Industrial Statistics Revisited: From Footnotes to Meta-Information Management. Austrian Journal of Statistics 31(1): 9–34, 2002.

K.A. Froeschl, W. Grossmann, V. Del Vecchio The Concept of Statistical Metadata. Project Deliverable D5, MetaNet (IST-1999-29093) Workgroup 2 (Harmonization of Metadata: Structure and Definitions), vi+134 pages, 2003.

W. Grossmann and K.A. Froeschl. Automated Table Generation Through Metadata (in German). Project Report, Dept. of Statistics, Univ. of Vienna, 160 pages, 1994.

U. Haag. Knowledge-Based Systems in Statistics: A Tutorial Overview with Examples. In P. Dirschedl, R. Ostermann (editors). Computational Statistics, pages 211-236. Physica (Springer), Heidelberg, 1994.

J. Han and M. Kamber. Data Mining—Concepts and Techniques. Morgan Kaufmann, San Francisco et al., 2001.

F. Inglese and F. Oropallo. The Development of an Integrated and Systematized Information System for Economic and Policy Impact Analysis. Austrian Journal of Statistics, this issue.

A.W. Kimball. Errors of the Third Kind in Statistical Consulting. J. Amer. Statistical Association 52: 133-142, 1957.

P. Ofner. Embedding of Weighting Algorithms into Metadata Structures. Dissertation Thesis, Dept. of Statistics and Decision Support Systems, University of Vienna, 2001.

J. Ryssevik. Metadata for Traveling Statistics—The World of Statistics Meets the Semantic Web. Invited Talk at the 14th Int. Conf. On Scientific and Statistical Database Management (Edinburgh, UK), 2002.

F.J. Scheuren. Macro and Micro Paradata for Survey Assessment. Paper presented in a satellite meeting to the UN/ECE Work Session on Statistical Metadata (Washington D.C., November 2000), U.S. Bureau of Labor Statistics. 16 pages, 2000.

M. Silver. The Role of Footnotes in a Statistical Metainformation System. Statistical Journal of the United Nations Economic Commission for Europe 10(2): 153-170, 1993.

B. Streitberg. On the Non-Existence of Expert Systems—Critical Remarks on Artificial Intelligence in Statistics. Stat. Software Newsletter 14(2): 55-62, 1988.

B. Sundgren. An Infological Approach to Data Bases. Report, Statistics Sweden, 1973.

J.W. Tukey. Exploratory Data Analysis. Addison-Wesley, Ma., 1977.

W.E. Winkler. Matching and Record Linkage. In Cox B.G. (editor). Business Survey Methods, pages 355-384. Wiley, New York, 1995.

G. Wiederhold and M. Genesereth. The Conceptual Basis for Mediation Services. IEEE Expert 12(5), 38-47.