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THE METHOD FOR SEMANTICALLY ORIENTED BIG DATA MODELLING IN ACCORDANCE WITH CONTEXT
Abstract
The existing approach to organizing big data processing requires the analyst to understand the set of initial data. This understanding presupposes the presence of knowledge not only about the list of available characteristics, their fullness, noisiness, etc., but also about the nature of their occurrence - the semantics of the data. Big data semantics can be represented using context. The context in the project refers to the activity in which the data is used or because of which was obtained. Applied scientific approaches that are relevant today, allowing to build models considering semantics and (or) context, are based on the concept of generalization - the same in meaning, but differently named data values are combined under some generalizing descriptive construction. This primarily leads to a simplified presentation of the content of the big data store, since semantics and context are used to identify homogeneous values. These models provide the analyst with a scaled-down view of the big data warehouse in terms of existing models, while the semantics and context are still hidden. The paper proposes an analytics-oriented technology for modeling big data as possible data acquisition actions. The main feature of this technology is that the initiator of filling or creating a big data warehouse is directly the analyst as the main consumer of information. As a result of such structuring of the process in the context of analysis, the need to present the data available in the warehouse will no longer make sense, since this model will be initially determined by the analyst. And the data itself, presented in the storage, will correspond to the purposes of the analysis, and thus storage will be optimized.
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