Recommendation ITU-T Y.3601 provides a framework for data exchange in a big data ecosystem. Big data exchange covers multiple processes for data import and data export within a big data ecosystem. Big data exchange is used for exchanging data of multiple types and multiple formats from a data source to a data target.
This Recommendation specifies framework of big data driven networking. The scope of this recommendation includes the model architecture of big data driven networking (bDDN), the high-level capabilities of bDDN, the interface capabilities among different planes and layers.
Recommendation ITU-T F.743.7 specifies requirements for visual surveillance enhanced by big data (VSBD) services. It promotes the value of visual surveillance services by using big data analytics method and tools. Massive video, event and sensing data are analysed to support enhanced visual surveillance services, including video retrieval, event detection and status prediction.
ISO/IEC TR 20547-2:2018 provides examples of big data use cases with application domains and technical considerations derived from the contributed use cases.
The present document specifies a data model for NFV descriptors, using the TOSCA Simple Profile in YAML, fulfilling the requirements specified in ETSI GS NFV-IFA 011 and ETSI GS NFV-IFA 014 for a Virtualised Network Function Descriptor (VNFD), a Network Service Descriptor (NSD) and a Physical Network Function Descriptor (PNFD). The present document also specifies requirements on the VNFM and NFVO specific to the handling of NFV descriptors based on the TOSCA Simple Profile in YAML specification.
The present document specifies the structure of the Network Service Descriptor (NSD) file archive and the naming conventions for the different files it contains, fulfilling the requirements specified in ETSI GS NFV-IFA 014 [1] for an NSD file structure.
My fellowship tackles the gap related to the insufficient models for achieving analysis-ready geo data.
Since graph database technology is a key for meaningful and explainable machine learning and generative AI, it is evident that the GQL standard will have positive impact on applications in our societies.
