EXTENSIONS TO THE OLAP FRAMEWORK FOR BUSINESS ANALYSIS

Emiel Caron, Hennie Daniels

2008

Abstract

In this paper, we describe extensions to the OnLine Analytical Processing (OLAP) framework for business analysis. This paper is part of our continued work on extending multi-dimensional databases with novel functionality for diagnostic support and sensitivity analysis. Diagnostic support offers the manager the possibility to automatically generate explanations for exceptional cell values in an OLAP database. This functionality can be built into conventional OLAP databases using a generic explanation formalism, which supports the work of managers in diagnostic processes. The objective is the identification of specific knowledge structures and reasoning methods required to construct computerized explanations from multi-dimensional data and business models. Moreover, we study the consistency and solvability of OLAP systems. These issues are important for sensitivity analysis in OLAP databases. Often the analyst wants to know how some aggregated variable in the cube would have been changed if a certain underlying variable is increased ceteris paribus (c.p.) with one extra unit or one percent in the business model or dimension hierarchy. For such analysis it is important that the system of OLAP aggregations remains consistent after a change is induced in some variable. For instance, missing data, dependency relations, and the presence of non-linear relations in the business model can cause a system to become inconsistent.

References

  1. E. Caron, H.A.M. Daniels, (2007). Explanation of exceptional values in multidimensional databases. European Journal of Operational Research, 188, 884- 897.
  2. A.J. Feelders, “Diagnostic reasoning and explanation in financial models of the firm”, PhD thesis, Tilburg University (1993).
  3. A.J. Feelders, H.A.M. Daniels, “Theory and methodology: a general model for automated business diagnosis”, European Journal of Operational Research, 623-637, (2001).
  4. G. Hesslow, Explaining differences and weighting causes, Theoria 49 (1983) 87-111.
  5. D.C. Hoaglin, F. Mosteller, J.W. Tukey, Exploring Data Tables, Trends and Shapes, Wiley series in probability, New York, 1988.
  6. P.W. Humphreys, The Chances of Explanation, Princeton University Press, Princeton, New Jersey, 1989.
  7. H.J. Lenz, A. Shoshani, (1997). Summarizability in OLAP and statistical data bases, Statistical and Scientific Database Management, 132-143.
  8. N.S. Koutsoukis, G. Mitra, C. Lucas (1999). Adapting online analytical processing for decision modelling: The interaction of information and decision technologies, Decision Support Systems 26 (1) 1-30.
  9. S. Sarawagi, R. Agrawal, R. Megiddo, (1998) Discoverydriven exploration of OLAP data cubes, in: Conf. Proc. EDBT 7898, London, UK, pp. 168-182.
  10. H. Scheffé, (1959) The Analysis of Variance, Wiley, New York.
  11. W.J. Verkooijen, (1993) Automated financial diagnosis: A comparison with other diagnostic domains, Journal of Information Science 19 (2), 125-135, May.
Download


Paper Citation


in Harvard Style

Caron E. and Daniels H. (2008). EXTENSIONS TO THE OLAP FRAMEWORK FOR BUSINESS ANALYSIS . In Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT, ISBN 978-989-8111-53-1, pages 240-247. DOI: 10.5220/0001895102400247


in Bibtex Style

@conference{icsoft08,
author={Emiel Caron and Hennie Daniels},
title={EXTENSIONS TO THE OLAP FRAMEWORK FOR BUSINESS ANALYSIS},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,},
year={2008},
pages={240-247},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001895102400247},
isbn={978-989-8111-53-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 3: ICSOFT,
TI - EXTENSIONS TO THE OLAP FRAMEWORK FOR BUSINESS ANALYSIS
SN - 978-989-8111-53-1
AU - Caron E.
AU - Daniels H.
PY - 2008
SP - 240
EP - 247
DO - 10.5220/0001895102400247