Cloud computing
Cloud computing. ISO/IEC 19944 is applicable primarily to cloud service providers, cloud service customers and cloud service users, but also to any person or organization involved in legal, policy, technical or other implications of data flows between devices and cloud services."
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.
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) 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.
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.
ISO 19117:2012 specifies a conceptual schema for describing symbols, portrayal functions that map geospatial features to symbols, and the collection of symbols and portrayal functions into portrayal catalogues. This conceptual schema can be used in the design of portrayal systems. It allows feature data to be separate from portrayal data, permitting data to be portrayed in a dataset independent manner.
ISO 19118:2011 specifies the requirements for defining encoding rules for use for the interchange of data that conform to the geographic information in the set of International Standards known as the _ISO 19100 series. ISO 19118:2011 specifies requirements for creating encoding rules based on UML schemas, requirements for creating encoding services, and requirements for XML-based encoding rules for neutral interchange of data. ISO 19118:2011 does not specify any digital media, does not define any transfer services or transfer protocols, nor does it specify how to encode inline large images.
ISO 19119:2016 defines requirements for how platform neutral and platform specific specification of services shall be created, in order to allow for one service to be specified independently of one or more underlying distributed computing platforms. ISO 19119:2016 defines requirements for a further mapping from platform neutral to platform specific service specifications, in order to enable conformant and interoperable service implementations. ISO 19119:2016 addresses the Meta:Service foundation of the ISO geographic information reference model described in ISO 19101‑1:2014, Clause 6 and Clause 8, respectively. ISO 19119:2016 defines how geographic services shall be categorised according to a service taxonomy based on architectural areas and allows also for services to be categorised according to a usage life cycle perspective, as well as according to domain specific and user defined service taxonomies, providing support for easier publication and discovery of services.
ISO 19109:2015 defines rules for creating and documenting application schemas, including principles for the definition of features.The scope of this International Standard includes the following:(1) conceptual modelling of features and their properties from a universe of discourse;(2) definition of application schemas;(3) use of the conceptual schema language for application schemas;(4) transition from the concepts in the conceptual model to the data types in the application schema; and(5) integration of standardized schemas from other ISO geographic information standards with the application schema.The following are outside the scope:(a) choice of one particular conceptual schema language for application schemas;(b) definition of any particular application schema;(c) representation of feature types and their properties in a feature catalogue;(d) representation of metadata;(e) rules for mapping one application schema to another;(f) implementation of the application schema in a computer environment;(g) computer system and application software design; and(h) programming.
This document specifies a schema for feature concept dictionaries to be established and managed as registers. It does not specify schemas for feature catalogues or for the management of feature catalogues as registers. However, as feature catalogues are often derived from feature concept dictionaries, this document does specify a schema for a hierarchical register of feature concept dictionaries and feature catalogues. These registers are in accordance with ISO 19135‑1.