Big Data & Open data

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor- laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (NBDIF) series of volumes. This volume, Volume 2, contains the Big Data taxonomies developed by the NBD-PWG. These taxonomies organize the reference architecture components, fabrics, and other topics to lay the groundwork for discussions surrounding Big Data.

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (NBDIF) series of volumes. This volume, Volume 4, contains an exploration of security and privacy topics with respect to Big Data. The volume considers new aspects of security and privacy with respect to Big Data, reviews security and privacy use cases, proposes security and privacy taxonomies, presents details of the Security and Privacy Fabric of the NIST Big Data Reference Architecture (NBDRA), and begins mapping the security and privacy use cases to the NBDRA.

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor-laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework series of volumes. This volume, Volume 5, presents the results of the reference architecture survey. The reviewed reference architectures are described in detail, followed by a summary of the reference architecture comparison.

Big Data is a term used to describe the large amount of data in the networked, digitized, sensor- laden, information-driven world. While opportunities exist with Big Data, the data can overwhelm traditional technical approaches and the growth of data is outpacing scientific and technological advances in data analytics. To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data. The results are reported in the NIST Big Data Interoperability Framework (NBDIF) series of volumes. This volume, Volume 7, contains summaries of the work presented in the other six volumes, an investigation of standards related to Big Data, and an inspection of gaps in those standards.

The OASIS MQTT TC is producing a standard for the Message Queuing Telemetry Transport Protocol compatible with MQTT V3.1, together with requirements for enhancements, documented usage examples, best practices, and guidance for use of MQTT topics with commonly available registry and discovery mechanisms. The standard supports bi-directional messaging to uniformly handle both signals and commands, deterministic message delivery, basic QoS levels, always/sometimes-connected scenarios, loose coupling, and scalability to support large numbers of devices. Candidates for enhancements include message priority and expiry, message payload typing, request/reply, and subscription expiry.

The OASIS AMQP TC advances a vendor-neutral and platform-agnostic protocol that offers organizations an easier, more secure approach to passing real-time data streams and business transactions. The goal of AMQP is to ensure information is safely and efficiently transported between applications, among organizations, across distributed cloud computing environments, and within mobile infrastructures. AMQP avoids proprietary technologies, offering the potential to lower the cost of enterprise middleware software integrations through open interoperability. By enabling a commoditized, multi-vendor ecosystem, AMQP seeks to create opportunities for transforming the way business is done in the Cloud and over the Internet.

The present document describes the Single Illumination System, which allows to deliver Parent Signals for direct reception by consumer receivers and, at the same time, for a deterministic generation of daughter streams for terrestrial retransmission. Parent Signals can be provided to the daughter site via all defined TS-based DVB means - be it satellite, cable or terrestrial. Metadata may be provided as part of the Parent Signal(s) (called "in-band" in the present document). Part of the metadata may also be provided "out-of-band".

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.

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.

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.

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.