Standard

This International Standard provides a model and framework for integrating different standards as well as systems based on those specifications by supporting the use case specific identification and consistent, formal representation including constraints of necessary components and their relationships. It facilitates analysis and improvement of specifications under revision as well as the design of new projects. The approach is future proof due to its scientific soundness, based on systems theory, knowledge representation and knowledge management via ontology development and harmonization, that way supporting advanced interoperability between dynamic, multi-domain systems through knowledge and skills sharing in the context of intelligent cooperation. The approach is successfully deployed in several standards such as ISO 22600, ISO 21298, ISO 13606, ISO 12967, ISO 13940 and ISO 13972 (both under way), but also in most of the HL7 security specifications. The intended International Standard adopts objectives, content and presentation style used in other foundational standards such as ISO/IEC 10746, this way qualifying for a potential ISO/IEC 10746-6

This document specifies a set of representational primitives and semantic relations needed for an unambiguous representation of explicit time-related expressions in health informatics. This document does not introduce or force a specific ontology of time, nor does it force the use of a fixed representation scheme for such an ontology. Rather this document provides a set of principles for syntactic and semantic representation that allow the comparability of specific ontologies on time and the exchange of time-related information that is expressed explicitly. This document applies to both the representation of actual phenomena occurring in the real world (e.g. registrations in medical records) and to the description of concepts (e.g. medical knowledge bases). This document is applicable to a) developers of medical information systems where there might be a need for explicit time-related concepts for internal organization (e.g. temporal databases, temporal reasoning systems), b) information modellers or knowledge engineers building models for the systems mentioned in a), c) experts involved in the development of semantic standards on precise subdomains in health care where time-related information needs to be covered, (e.g. in the study of pathochronology, i.e. the discipline dealing with the time course of specific diseases), and d) developers of interchange formats for messages in which time-related information is embedded. This document is not intended to be used directly for — representing what is true in time, — reasoning about time, or — representation of metrological time.

ISO/IEC 25024:2015 defines data quality measures for quantitatively measuring the data quality in terms of characteristics defined in ISO/IEC 25012.

ISO/IEC 25024:2015 contains the following:

- a basic set of data quality measures for each characteristic;

- a basic set of target entities to which the quality measures are applied during the data-life-cycle;

- an explanation of how to apply data quality measures;

- a guidance for organizations defining their own measures for data quality requirements and evaluation.

It includes, as informative annexes, a synoptic table of quality measure elements defined in this International standard (Annex A), a table of quality measures associated to each quality measure element and target entitiy (Annex B), considerations about specific quality measure elements (Annex C), a list of quality measures in alphabetic order (Annex D), and a table of quality measures grouped by characteristics and target entities (Annex E).

This International Standard does not define ranges of values of these quality measures to rate levels or grades because these values are defined for each system by its nature depending on the system context and users' needs.

This International Standard can be applied to any kind of data retained in a structured format within a computer system used for any kinds of applications.

People managing data and services including data are the primary beneficiaries of the quality measures.

This International Standard is intended to be used by people who need to produce and/or use data quality measures while pursuing their responsibilities.

- Acquirer (an individual or organization that acquires or procures data from a supplier).

- Evaluator (an individual or organization that performs an evaluation, which can, for example, be a testing laboratory, the quality department of an organization, a government organization, or a user).

- Developer (an individual or organization that performs development activities including requirements, analysis, design, implementation, and testing data during the data-life-cycle).

- Maintainer (an individual or organization that performs operation and maintenance activities of data).

- Supplier (an individual or organization that enters into a contract with the acquirer for the supply of data or service under the terms of the contract).

- User (an individual or organization that uses data to perform a specific function).

- Quality manager (an individual or organization that performs a systematic examination of the data).

- Owner (an individual or organization that takes responsibility for the management and financial value of the data with the legal authority and responsibility to establish for them evaluation, collections, access, dissemination, storage, security, and cancellation).

ISO/IEC 25024:2015 takes into account a large range of data of target entities.

It can be applied in many types of information systems, for example, such as follows:

- legacy information system;

- data warehouse;

- distributed information system;

- cooperative information system;

- world wide web.

The scope does not include the following:

- knowledge representation;

- data mining techniques;

- statistical significance for random sample.

Defines quality criteria

→ Functionality: Correct-ness, appropriateness, completeness

→ Reliability: Maturity, fault tolerance, recoverability

→Usability: Understandabil-ity, operability, learnability, robustness

→Efficiency: economic efficiency, behaviour over time, consumption pattern

→Maintainability: Analysa-bility, modifiability, stability, testability

→Portability: Adaptability, installability, conformity, replaceability

→Security: integrity, confidentiality, authenticity, accountability, non-repudiation

→Compatibility: interoperability (can be used to draw up the specification and test cases

La serie ISO/IEC 25000 è stata sviluppata dall'ISO/IEC JTC1 SC7 Working Group WG6 "Software product and system quality". La prima riunione del WG6 si svolse a Torino nel 1991, anno di pubblicazione dell'ISO/IEC 9126-1, standard precursore dell'ISO/IEC 25010 che dal 2011 lo sostituisce.

L'ISO/IEC 25000 è "estensibile" ed applicabile a vari domini e tendenzialmente "user friendly". Inoltre nel campo delle nuove tecnologie si sta valutando dal 2020 l'estensione dei modelli di qualità nel campo dell'intelligenza artificiale.

ISO/IEC TR 20547-5:2018 describes big data relevant standards, both in existence and under development, along with priorities for future big data standards development based on gap analysis.

This document specifies the big data reference architecture (BDRA). The reference architecture includes concepts and architectural views.

The reference architecture specified in this document defines two architectural viewpoints:

— a user view defining roles/sub-roles, their relationships, and types of activities within a big data ecosystem;

— a functional view defining the architectural layers and the classes of functional components within those layers that implement the activities of the roles/sub-roles within the user view.

The BDRA is intended to:

— provide a common language for the various stakeholders;

— encourage adherence to common standards, specifications, and patterns;

— provide consistency of implementation of technology to solve similar problem sets;

— facilitate the understanding of the operational intricacies in big data;

— illustrate and understand the various big data components, processes, and systems, in the context of an overall big data conceptual model;

— provide a technical reference for government departments, agencies and other consumers to understand, discuss, categorize and compare big data solutions; and

— facilitate the analysis of candidate standards for interoperability, portability, reusability, and extendibility.

SO/IEC TR 20547-2:2018 provides examples of big data use cases with application domains and technical considerations derived from the contributed use cases.

This document describes the framework of the big data reference architecture and the process for how a user of the document can apply it to their particular problem domain.

This document provides a set of terms and definitions needed to promote improved communication and understanding of this area. It provides a terminological foundation for big data-related standards.

This document provides a conceptual overview of the field of big data, its relationship to other technical areas and standards efforts, and the concepts ascribed to big data that are not new to big data

ISO/IEC 18045:2008 is a companion document to ISO/IEC 15408, Information technology - Security techniques - Evaluation criteria for IT security. ISO/IEC 18045:2008 defines the minimum actions to be performed by an evaluator in order to conduct an ISO/IEC 15408 evaluation, using the criteria and evaluation evidence defined in ISO/IEC 15408. ISO/IEC 18045:2008 does not define evaluator actions for certain high assurance ISO/IEC 15408 components, where there is as yet no generally agreed guidance.

Recommendation ITU-T Y.3170 specifies requirements for machine learning-based quality of service (QoS) assurance for the International Mobile Telecommunications 2020 (IMT-2020) network.

Recommendation ITU-T Y.3170 first provides an overview of machine learning-based QoS assurance for the IMT‑2020 network. Recommendation ITU-T Y.3170 includes an overview of capabilities for QoS anomaly detection and prediction using machine learning. Recommendation ITU-T Y.3170 then describes a functional model of machine learning-based QoS assurance that includes functional components such as QoS data collection; data pre-processing; data storage, modelling and training; QoS anomaly detection and prediction; QoS policy decision making; enforcement; and reporting. Based on the capabilities and functionalities described in the functional model, Recommendation ITU-T Y.3170 specifies high-level requirements and functional requirements for machine learning-based QoS assurance for the IMT-2020 network.