This document provides a collection of representative use cases of AI applications in a variety of domains.

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  • Draft
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This document provides background about existing methods to assess the robustness of neural networks.

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    31 pages
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    31 pages
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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.

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This document surveys topics related to trustworthiness in AI systems, including the following: — approaches to establish trust in AI systems through transparency, explainability, controllability, etc.; — engineering pitfalls and typical associated threats and risks to AI systems, along with possible mitigation techniques and methods; and — approaches to assess and achieve availability, resiliency, reliability, accuracy, safety, security and privacy of AI systems. The specification of levels of trustworthiness for AI systems is out of the scope of this document.

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  • Technical report
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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.

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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.

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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.

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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.

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    252 pages
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