Conference Service Mandl

abstract: While the worlds of science and business typically meet in the presence of a profitable scheme, individuals from both environments have interests in analyzing complex data about dynamic systems. Whether motivated by a drive to increase system efficiency or to understand nature, their shared goal leads to a shared focus on the underlying causal processes that explain or produce observed phenomena. To this end, researchers construct models from data derived from observed system behavior and background knowledge about the candidate processes. Traditional literature on regression, time-series analysis, and data mining produces descriptive models that may reproduce the observed data but cannot explain the principal dynamics. Therefore, researchers are called to develop methods that capture complex temporal and spatial relationships in terms of domain knowledge (e.g., relevant scientific or business concepts) and that construct these explanatory process models.