Operators for analyzing and modifying probabilistic data - a question of efficiency

Abstract

To enable analyses and decision support over historic, forecast, and estimated 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 introduces 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 examples in the context of a simple analysis process and discuss our findings and possible optimizations.