A computational model for refining Data domains in the property reconciliation

A computational model for refining of data domains which are selected out in the property recognition over the Big Data sources is developed and considered. Data sources can originate both from natural and/or human activities. Thus discovered in a problem domain data objects - the individuals, - are considered as processes in a mathematical sense depending on parameters. The proposed parametrization is based on two-dimensional model using cross-referencing over assignments/crowdsoucers and recognizable properties/domains and is aimed to support the iteration procedure. This gives rise to the computational model based on the variable domains assumption. Such a vision is able to take into account the interaction of crowdsourcers and properties when they are varying with the evolving the events. The property recognition stage-by-stage model enables the fine tuning of the target data domains and has the representable functor. This model as may be shown is faithfully embedded into a category of indexed sets. The proposed (f, g)-tuning of the data domains leads to a neighborhood structure for cognition activity and gives a flexible computing model.