Sensitivity analysis in OLAP databases

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Abstract

The theoretical underpinnings under which sensitivity analysis is valid in OLAP databases are dealt with in this paper. Sensitivity analysis is considered to be the reverse of explanation generation in diagnostic reasoning. Our exposition differentiates between sensitivity analysis in systems of purely drill-down equation and mixed systems of equations with also business model equations. It is proven that there is an unique additive drilldown measure defined on all cubes of the aggregation lattice. This proof is the basis for sensitivity analysis in OLAP databases, where a change in some base cell in the lattice is propagated to all descendants in its upset. For sensitivity analysis in mixed systems of equations a matrix notation is presented and the conditions for solvability are discussed. Due to the fact that such systems are typically overdetermined in OLAP databases, the implicit function theorem cannot be applied. Therefore, we proposed a method to reduce the number of equations in the system and apply the implicit function theorem on a subsystem of the original system. We conclude with an alternative method for what-if analysis in mixed systems of equations.
Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018)
Place of PublicationMadeira
PublisherSciTePress
Pages221-228
ISBN (Electronic)9789897582981
DOIs
Publication statusPublished - 2018
Event20th International Conference on Information Systems - Funchal, Madeira, Portugal
Duration: 21 Mar 201824 Mar 2018

Conference

Conference20th International Conference on Information Systems
Abbreviated titleICEIS 2018
CountryPortugal
CityFunchal, Madeira
Period21/03/1824/03/18

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Keywords

  • OLAP Databases, Business Analytics, Explanatory Analytics, Sensitivity Analysis, Decision-support Systems

Cite this

Caron, E., & Daniels, H. (2018). Sensitivity analysis in OLAP databases. In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018) (pp. 221-228). SciTePress. https://doi.org/10.5220/0006791702210228