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. The growth of data is outpacing scientific and technological advances in data analytics. Opportunities exist with Big Data to address the volume, velocity and variety of data through new scalable architectures. 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 1, contains a definition of Big Data and related terms necessary to lay the groundwork for discussions surrounding Big Data.

The potential for organizations to capture value from Big Data improves every day as the pace of the Big Data revolution continues to increase, but the level of value captured by companies deploying Big Data initiatives has not been equivalent across all industries. Most companies are struggling to capture a small fraction of the available potential in Big Data initiatives. The healthcare and manufacturing industries, for example, have so far been less successful at taking advantage of data and analytics than other industries such as logistics and retail. Effective capture of value will likely require organizational investment in change management strategies that support transformation of the culture, and redesign of legacy processes. In some cases, the less-than-satisfying impacts of Big Data projects are not for lack of significant financial investments in new technology. It is common to find reports pointing to a shortage of technical talent as one of the largest barriers to undertaking projects, and this issue is expected to persist into the future. This volume explores the adoption of Big Data systems and barriers to adoption; factors in maturity of Big Data projects, organizations implementing those projects, and the Big Data technology market; considerations for implementation and modernization of Big Data systems; and, Big Data readiness.

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 6, summarizes the work performed by the NBD-PWG to characterize Big Data from an architecture perspective, presents the NIST Big Data Reference Architecture (NBDRA) conceptual model, and discusses the components and fabrics of the NBDRA.

This document summarizes interfaces that are instrumental for the interaction with Clouds, Containers, and High Performance Computing (HPC) systems to manage virtual clusters to support the NIST Big Data Reference Architecture (NBDRA). The REpresentational State Transfer (REST) paradigm is used to define these interfaces, allowing easy integration and adoption by a wide variety of frameworks. Big Data is a term used to describe extensive datasets, primarily in the characteristics of volume, variety, velocity, and/or variability. While opportunities exist with Big Data, the data characteristics 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 8, uses the work performed by the NBD-PWG to identify objects instrumental for the NIST Big Data Reference Architecture (NBDRA) which is introduced in the NBDIF: Volume 6, Reference Architecture.

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) worked 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 3, contains the original 51 Version 1 use cases gathered by the NBD-PWG Use Cases and Requirements Subgroup and the requirements generated from those use cases. The use cases are presented in their original and summarized form. Requirements, or challenges, were extracted from each use case, and then summarized over all the use cases. These generalized requirements were used in the development of the NIST Big Data Reference Architecture (NBDRA), which is presented in Volume 6. During the development of Version 2 of the NBDIF, the Use Cases and Requirements Subgroup and the Security and Privacy Subgroup identified the need for additional use cases to strengthen work of the NBD-PWG in Stage 3. The subgroup accepted additional use case submissions using the more detailed Use Case Template 2. The three additional use case submissions collected using Use Case Template 2 are presented and summarized in this volume.

Big data based on cloud computing provides the capabilities to collect, store, analyze, visualize and handle varieties of large volume datasets, which cannot be rapidly transferred and analyzed using traditional technologies. e.g. Big Data as a Service (as defined in [ITU-T Y.3600], big data as a service (BDaaS) is a cloud service category in which the capabilities provided to the cloud service customer are the ability to collect, store, analyse, visualize and manage data using big data.). Data storage, analysis, calculation and other data services based on the big data platform, are developing rapidly in recent years.

The present document's scope is to provide guidance on OTT video streaming testing approach with a set of minimum desired and most meaningful QoE centric QoS parameters along with recommendations to create a figure of merit quantifying the OTT video streaming session quality, where possible. In addition, the set of introduced QoE centric QoS parameters aim to help with the identification of the possible roots of video quality degradation. The present document also offers means to understand aspects related with network and services optimization and troubleshooting, such as the trade-off between bandwidth usage or controlled throttling and end-to-end video quality.

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 provides a framework for concurrent tests of multiple services, using a top-level approach which is also modular and scalable with respect to new services. Also, the framework explicitly integrates measurement methodology, in particular reproducibility aspects.

The present document is part of a series of specifications that defines the fourth generation of high-speed data-over-cable systems, commonly referred to as the DOCSIS 3.1 specifications. The present document was developed for the benefit of the cable industry, and includes contributions by operators and vendors from North and South America, Europe and Asia.

The present document provides specifications of infrastructure related ITS services to support communication between infrastructure ITS equipment and traffic participants using ITS equipment (e.g. vehicles, pedestrians). It defines services in the Facilities layer for communication between the infrastructure and traffic participants. The specifications cover the protocol handling for infrastructure-related messages as well as requirements to lower layer protocols and to the security entity.

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.