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Human factors related context is structured into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user's social environment (co-location of others, social interaction, group dynamics), and the user's tasks (spontaneous activity, engaged tasks, general goals). Likewise, context related to physical environment is structured into three categories: location (absolute position, relative position, [[wikt:colocation|co-location]]), infrastructure (surrounding resources for computation, communication, task performance), and physical conditions (noise, light, pressure, air quality).<ref>[https://link.springer.com/article/10.1007%2Fs11042-010-0711-z?LI=true A Comprehensive Framework for Context-Aware Communication Systems. B. Chihani, E. Bertin, N. Crespi. 15th International Conference on Intelligence in Next Generation Networks (ICIN'11), Berlin, Germany, October 2011]</ref><ref>[https://ieeexplore.ieee.org/document/5956518 A Self-Organization Mechanism for a Cold Chain Monitoring System. C. Nicolas, M. Marot, M. Becker.  73rd Vehicular Technology Conference 2011 IEEE (VTC Spring), Yokohama, Japan May 2011]</ref>

Human factors related context is structured into three categories: information on the user (knowledge of habits, emotional state, biophysiological conditions), the user's social environment (co-location of others, social interaction, group dynamics), and the user's tasks (spontaneous activity, engaged tasks, general goals). Likewise, context related to physical environment is structured into three categories: location (absolute position, relative position, [[wikt:colocation|co-location]]), infrastructure (surrounding resources for computation, communication, task performance), and physical conditions (noise, light, pressure, air quality).<ref>[https://link.springer.com/article/10.1007%2Fs11042-010-0711-z?LI=true A Comprehensive Framework for Context-Aware Communication Systems. B. Chihani, E. Bertin, N. Crespi. 15th International Conference on Intelligence in Next Generation Networks (ICIN'11), Berlin, Germany, October 2011]</ref><ref>[https://ieeexplore.ieee.org/document/5956518 A Self-Organization Mechanism for a Cold Chain Monitoring System. C. Nicolas, M. Marot, M. Becker.  73rd Vehicular Technology Conference 2011 IEEE (VTC Spring), Yokohama, Japan May 2011]</ref>

== Relational context : dynamic and non-user-centric definitions==

== Relational context : dynamic and non-user-centric definitions==

Whereas early definitions of context tended to center on users, or devices interfaced directly with users, the oft-cited definition from Dey<ref name="dey2001"/> ("''any information that can be used to characterize the situation of an entity''") could be taken without this restriction. User-centric context, as may be used in the design of [[human-computer interaction|human-computer interfaces]], may also imply an overly clearcut, and partially arbitrary,  separation between "content" (anything which is ''explicitly'' typed in by users, or output to them), and context, which is ''implicit'', and used for [[context adaptation|adaptation]] purposes. A more dynamic and de-centered view, advocated by Dourish <ref> Dourish, Paul. "What we talk about when we talk about context." Personal and ubiquitous computing 8.1 (2004): 19-30.</ref> views context as primarily ''relational''. This was originally congruent with the move from desktop computing to [[ubiquitous computing]], but it does also fit with a broader understanding of [[ambient intelligence]] where the distinctions between ''context'' and ''content'' become relative and dynamic.<ref> https://www.researchgate.net/publication/230704197_Ambient_Intelligence Streitz, Norbert A., and Gilles Privat. "Ambient Intelligence" , ''Universal Access Handbook'' (2009)</ref> In this view, whichever sources of information (such as [[Internet of Things|IoT]] sensors) may be ''context'' for some uses and applications, might also be sources of primary ''content'' for others, and vice versa. What matters is the set of ''relationships'' that link them, together and with their environment. Whereas early descriptions of single-user-centric context could fit with classical [[Entity-attribute-value model|entity-attribute-value models]], more versatile graph-based information models, such as proposed with [[NGSI-LD]], are better adapted to capture the more relational view of context which is relevant for the  [[Internet of Things]], [[Cyber-Physical Systems]] and [[Digital Twins]]. In this broader acceptation, context is not only represented as a set of attributes attached to an entity, it is also captured by a graph that enmeshes this entity with others. Context awareness is the capability to account for this cross-cutting information from different sources.

Whereas early definitions of context tended to center on users, or devices interfaced directly with users, the oft-cited definition from Dey<ref name="dey2001"/> ("''any information that can be used to characterize the situation of an entity''") could be taken without this restriction. User-centric context, as may be used in the design of [[human-computer interaction|human-computer interfaces]], may also imply an overly clearcut, and partially arbitrary,  separation between "content" (anything which is ''explicitly'' typed in by users, or output to them), and context, which is ''implicit'', and used for [[context adaptation|adaptation]] purposes. A more dynamic and de-centered view, advocated by Dourish <ref> Dourish, Paul. "What we talk about when we talk about context." Personal and ubiquitous computing 8.1 (2004): 19-30.</ref> views context as primarily ''relational''. This was originally congruent with the move from desktop computing to [[ubiquitous computing]], but it does also fit with a broader understanding of [[ambient intelligence]] where the distinctions between ''context'' and ''content'' become relative and dynamic.<ref> https://www.researchgate.net/publication/230704197_Ambient_Intelligence Streitz, Norbert A., and Gilles Privat. "Ambient Intelligence" , ''Universal Access Handbook'' (2009)</ref> In this view, whichever sources of information (such as [[Internet of Things|IoT]] sensors) may be ''context'' for some uses and applications, might also be sources of primary ''content'' for others, and vice versa. What matters is the set of ''relationships'' that link them, together and with their environment. Whereas early descriptions of single-user-centric context could fit with classical [[Entity-attribute-value model|entity-attribute-value models]], more versatile graph-based information models, such as proposed with [[NGSI-LD]], are better adapted to capture the more relational view of context which is relevant for the  [[Internet of Things]], [[Cyber-Physical Systems]] and [[Digital Twins]]. In this broader acceptation, context is not only represented as a set of attributes attached to an entity, it is also captured by a graph that enmeshes this entity with others. Context awareness is the capability to account for this cross-cutting information from different sources.