Operators for Analyzing and Modifying Probabilistic Data – A Question of Efficiency
Jochen Adamek, Katrin Eisenreich, Volker Markl · 2015
Abstract: To enable analyses and decision support over historic, forecast, and es-timated data, efficient querying and modification of probabilistic data is an important aspect. In earlier work, we proposed a data model and operators for the analysis and the modification of uncertain data in support of what-if scenario analysis. Naturally, and as discussed broadly in previous research, the representation of uncertain data intro-duces additional complexity to queries over such data. When targeting the interactive creation and evaluation of scenarios, we must be aware of the run-time performance of the provided functionalities in order to better estimate response times and reveal potentials for optimizations to users. The present paper builds on our previous work, addressing both a comprehensive evaluation of the complexity of selected operators as well as an experimental validation. Specifically, we investigate effects of varying operator parameterizations and the underlying data characteristics. We provide exam-ples in the context of a simple analysis process and discuss our findings and possible optimizations. 1