BREAL Generic Information architecture BREAL Sample Clauses

BREAL Generic Information architecture BREAL. The purpose of this Section is to describe the BREAL Generic Information Architecture for Big Data (GIAB). GIAB consists of three layers, as described by the ‘hourglass model’ proposed for Trusted Smart Statistics, namely a raw data layer, a convergence layer, and a statistical layer: • The Raw Data Layer includes data that are acquired and stored by the BREAL “Acquisition and Recording” business function. Let us remark that, at this stage, the GIA just identifies the concepts, and no detail is provided on formats or other technical specifications that can be useful for raw data acquisition and storage. • The Convergence Layer contains data represented as units of interest for the analyses. These data are produced as results of the BREAL life cycle functions of “Data Wrangling” and “Data Representation”. • The Statistical Layer includes those concepts that are the targets of the analysis. These data are produced by “Modeling and Interpretation”, “Integrate Survey and Register Data”, “Enrich Statistical Registers” and “Shape Output”. In addition to the data concepts, some metadata concepts are introduced for each of the three layers. In particular, one specific category of metadata has been selected as very specific of Big Data, namely Provenance metadata. These metadata have been specified for each of the three layers above mentioned. Moreover, in order to better highlight the integration of the GIAB with respect to existing standards for data modelling in Official Statistics, we included some ‘key’ concepts from GSIM (Generic Statistical Information Model). The resulting composition of GIAB is shown in Figure 1. Each layer consists of: (i) specific BD data entities (blue color); (ii) GSIM entities (pink color) and (iii) specific provenance metadata entities (yellow color) . The Raw data layer of GIAB consists of: Specific BD data entities: a Big Data Source entity is introduced with three main characteristics that are considered as particularly relevant for characterizing the source itself, which indeed corresponds to volume, variety and velocity. Volume is taken into account by two entities, namely Big Data Size and Non-Big Data Size; variety, by the two entities Structured and Unstructured and velocity of data in acquisition phase, by the two entities In-Motion and At Rest. GSIM entities: GSIM Data Resource is introduced. A Big Data Source entity is a specialization of that. Specific provenance metadata entities: Two entities are introduced Acquired Ent...
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