This is one of the 52 terms in The Language of Technical Communication published by XML Press in 2016 and the contributor for this term is Christian Glahn.
What is it?
Technologies that infer the characteristics of one or more persons, locations, objects, situations, or activities and use that information to dynamically adapt to, synchronize, and frame situations and processes.
Why is it important?
Context sensing is a key technology for mobile systems, smart objects, and multi-device environments for extending and augmenting human-computer interactions.
Why does a technical communicator need to know this?
Integrating contextual information helps us create and deliver content that better meets user needs. For example, if a user performs a location-based search for nearby restaurants (location context) on a mobile phone, then it is likely that the best matches are those that are open at the time of the search (temporal context) and match the dietary needs of the user (identity context). The context of the user (location, time, dietary needs) is matched to that of the nearby restaurants (location, hours of operation, menu items).
Technical communicators need to understand how to describe these systems, known as context-aware or smart technologies, and how to use them for delivering context-aware content that can automatically infer information needs and user intentions.
Context sensing approaches can be categorized by their use of:
- Predefined information, including static data such as configurations or manually added information
- Sensor-based situational information, such as dynamic data that can be collected directly from hardware sensors connected to an information system
- Aggregated information, which integrates and enriches data from sensors or data sources.
Approaches that aggregate, combine, or integrate sensor information are also referred to as context detection. Approaches that can relate the contexts of different entities for identifying their situational similarity are called context matching. Context detection and matching typically operate on five groups of contextual information for enriching the user experience: individuality, activity, location, time and date, and relationships.