Datenschutzerklärung|Data Privacy
Impressum

25.05.2011
A. Borusan

06.06.2011, 16 Uhr c.t. Raum: EN 719, TU Berlin, Einsteinufer 17, 10587 Berlin: "Creation and Change Impact Analysis of What-if Scenarios under Uncertainty and Correlation" (Katrin Eisenreich, SAP Dresden)

When performing what-if analysis -- a technique increasingly applied in business planning and decision support -- both historic and hypothetic data (assumptions) play an important role. To construct scenarios, users apply operators to analyze, modify, and integrate both forms of data.
An important factor in this context is the handling of uncertainty and correlation in data, since they can have a major impact on analysis results. Besides, once a scenario has been created, it is important to enable users to investigate which assumptions were made to arrive at the scenario, and how possible changes in underlying data might influence its overall results.

Part I
In this talk, we first look at the specific aspect of correlation in data. I will present an approach that enables users to introduce arbitrary correlation structures to analyzed data, exploiting statistical methods well-established in financial and risk analysis. A central aspect of the discussed approach is the use of precomputed approximate correlation structures (ACRs) instead of sampling at run time. Thereby, we achieve faster processing of correlation queries and become independent from specific statistical library functions at query time. Further, the ACR approach opens up possibilities to efficient processing of subsequent operations over joint distributions, such as computing risk measures over the correlated data. We will introduce the construction and application of ACRs by means of an example scenario.

Part II
The second part of the talk focuses on the topic of scenario provenance. Apart from looking at the results of a scenario analysis, we must also allow users to trace back to where those results came from. For example, looking at a very high prediction for sales, a user should be able to see whether it is backed by some evidence (e.g., historic data) or comes mostly from very optimistic assumptions about the business or economic factors. Also, when actual data deviates from an applied assumption, the user should be able to see which impact this can have on the overall scenario.
In the talk, I will illustrate the capture and querying of provenance information based on a graph structure. Apart from information about the derivation process of data items, the discussed approach also takes into account the hypothetic nature of data. In particular, specific knowledge about analytic operators, such as for ACR-based correlation introduction, are exploited to allow for an efficient change impact analysis over executed scenarios.