Building a Smarter Planet™ requires creating an intelligent infrastructure that integrates technology with business, government, and the everyday life of the citizens of earth, to maximize the use of scarce resources, balance human use and ecosystem preservation, reduce costs, and improve quality of life. One of the keystones of this intelligent infrastructure is an integrative modeling framework (IMF), which is a platform for enabling the integration by nonexpert users of diverse sensor-based data, related business data, and complex cross-disciplinary mathematical modeling, in support of planning, monitoring, management, reporting, and decision support applications. We describe a research prototype that applies the Mashup Automation with Runtime Invocation and Orchestration (MARIO) technology from IBM Research to this problem in a specific application area in water management: simulating stream discharges using compositions of hydrologic process submodels derived from monolithic stream discharge simulators. We show how MARIO's semantic tagging and model composition engine enable us to meet three critical challenges of an IMF: 1) generating valid chains or compositions of model components, given a definition of starting and ending states; 2) allowing all scientifically valid compositions of components; and 3) disallowing compositions that are scientifically invalid, i.e., that combine model components whose basic assumptions about quantities such as soil architectures or evaporation schemes conflict. While we focus here on water management, the technology that we describe can readily be generalized to other intelligent infrastructure applications, e.g., cities, transportation, and utilities.
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