Diploma and Master Theses (authored and supervised):
"Data Cleaning and Performance Tuning in the GAINS Model";
Supervisor: R. Pichler, K. Seyr;
Institut für Informationssysteme, Arbeitsbereich Datenbanken & Artificial Intelligence,
final examination: 2008-04-18.
This presentation will discuss how data cleaning and performance tuning can positively affect a database driven application. Based on the example of the GAINS model that was developed by the Transboundary Air Pollution (TAP) program at the International
Institute for Applied Systems Analysis (IIASA) on the one hand to provide a consistent framework for the analysis of emission reduction strategies, focusing on acidiﬁcation, eutrophication, and tropospheric ozone, on the other hand to meet the needs of "pollution science" as well as to model the pollution through greenhouse gases.
The author will give various examples of actions taken that not only improve the application from the data modeling point of view, but also guarantee the correctness and completeness of the data presented by the model. Additionally it will be shown that the measures taken lower the maintenance effort and do not slow the model down. The introduced changes also set the stage for the introduction of new concepts like data warehousing or web services in the near future.
Created from the Publication Database of the Vienna University of Technology.