Defining Well-Known Uniform Resource Identifiers (URIs)

Nottingham, Mark; Hammer-Lahav, Eran

doi:10.17487/rfc5785

Cited by 47 publications

(40 citation statements)

References 2 publications

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“…The following subsections describe two methods of service discovery using DNS SRV records [RFC2782] and an HTTP "well-known" [RFC5785] resource. However, alternative mechanisms could also be used (e.g., a DHCP server option [RFC2131]).…”

Section: Discoverymentioning

confidence: 99%

“…Clients MUST handle HTTP redirects on the ".well-known" URI, taking into account security restrictions on redirects described in Section 8. Servers MUST NOT locate the actual time zone data distribution service endpoint at the ".well-known" URI as per Section 1.1 of [RFC5785]. The "well-known" URI MUST be present on the server, even when a TXT RR (Section 4.2.1.2) is used in the DNS to specify a "context path".…”

Section: Txt Records For a Time Zone Data Distribution Servicementioning

confidence: 99%

“…This specification defines a new registry of "actions" for the time zone data distribution service protocol, defines a "well-known" URI using the registration procedure and template from Section 5.1 of [RFC5785], creates two new SRV service label aliases, and defines one new iCalendar property parameter as per the registration procedure in [RFC5545]. It also adds a new "TZDIST Identifiers Registry" to the IETF parameters URN sub-namespace as per [RFC3553] for use with protocol related error codes.…”

Section: Iana Considerationsmentioning

confidence: 99%

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Time Zone Data Distribution Service

Douglass¹,

Daboo²

2016

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Section: Discoverymentioning

confidence: 99%

Section: Txt Records For a Time Zone Data Distribution Servicementioning

confidence: 99%

Section: Iana Considerationsmentioning

confidence: 99%

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Time Zone Data Distribution Service

Douglass¹,

Daboo²

2016

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“…In such environments, the possibility of discovering resources, hosted by constrained nodes, is important for applications to run without human intervention and for flexible interfaces to be provided. Web discovery and linking was initially specified for Hypertext Transfer Protocol (HTTP) in [6], [7]. In the context of constrained nodes, the discovery of resources, their attributes and relations is referred to as CoRE Resource Discovery [8].…”

Section: Coap and Corementioning

confidence: 99%

“…CoAP provides a request/response interaction model between application endpoints. The "coap" and "coaps" URI schemes are used to identify CoAP resources and the path prefix "/.well-known" is supported for clients to be able to discover resources available at the host, or discover any policy/information about the host, before requesting for resource values/notifications [7].…”

Section: A Coapmentioning

confidence: 99%

RELOAD/CoAP architecture with resource aggregation/disaggregation service

Rodrı́gues

Guerreiro

Correia

2016

2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-M2M communication is expected to occur at a global level and for this reason federations of device networks are also expected. In such large scale environments, a critical issue is how to discover the available resources in a scalable manner. For this purpose CoAP Usage for RELOAD, a generic selforganizing P2P overlay network service, has been proposed to be used as a lookup service, to store available resources and as a cache for sensor data. However, such approach alone does not allow building an aggregate resource hierarchy, a very relevant issue for an efficient organization of data in future IoT applications. Here we address this issue and propose an architecture incorporating a resource aggregation/disaggregation service.

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Providing assurance and scrutability on shared data and machine learning models with verifiable credentials

Barclay

Preece

Taylor

et al. 2022

Concurrency and Computation

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Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This article introduces a software architecture and implementation of a system based on design patterns from the field of self-sovereign identity.Scientists can issue signed credentials attesting to qualities of their data resources.Data contributions to ML models are recorded in a bill of materials (BOM), which is stored with the model as a verifiable credential. The BOM provides a traceable record of the supply chain for an AI system, which facilitates on-going scrutiny of the qualities of the contributing components. The verified BOM, and its linkage to certified data qualities, is used in the AI scrutineer, a web-based tool designed to offer practitioners insight into ML model constituents and highlight any problems with adopted datasets, should they be found to have biased data or be otherwise discredited.

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Defining Well-Known Uniform Resource Identifiers (URIs)

Cited by 47 publications

References 2 publications

Time Zone Data Distribution Service

Time Zone Data Distribution Service

RELOAD/CoAP architecture with resource aggregation/disaggregation service

Providing assurance and scrutability on shared data and machine learning models with verifiable credentials

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