Context is the unstated actor in human communications, actions, and situations. It makes our communications efficient, our commands actionable, and our situations understandable by those—devices, people, or organizations—seeking to provide us with content or services. The advent of context-aware computing is therefore concomitant with the increased embedding of technology into our personal and social environments.

Why Context-Aware Computing?

Proactive, contextualized delivery of information, alerts, and advertisements (including content and product recommendations) represents significant commercial opportunities for all types of service providers. It stands to drive the user experience of mobile consumers well beyond having to search for information and beyond location-based services that adapt to mere vestiges of our larger context.

Context mediation also enables orchestration of Web services, thereby yielding actionable interpretation of spoken high-level commands, a key technology in creating truly smart personal assistants. It stands to deliver novel conversational affordances into interfaces ranging from devices to public display kiosks.

Comprehensive capture, representation, communication, gathering, and brokering of the larger user context, according to Gartner (A. Johnson, J. O’Brien, and G. Alvarez, Context Shapes Demand at Moments of Truth, Industry Research report ID: G00200528, Gartner, Inc., 25 May 2010), represents billions of dollars per year of revenue opportunity in industries such as travel and retail. Context awareness sharpens relevance when responding to user-initiated actions (such as product search and support calls). It also enables proactive communications through pattern analytics over both user behavior and user environment.

Physical or sensed context is captured using GPS and other devices. It is decorated using linked open data, other aspects of user-provided context, and social networks, and further analyzed, in complex commercial ecosystems of context providers and brokers. This derived context becomes the target of content providers and advertisers, who receive context views of users created using context caching, indexing, and partitioning algorithms. The content and services can either be pushed into context by the provider or pulled into it by the user.

Furthermore, context is itself a type of data for use by various operations, such as resolving ambiguous references, resolving inconsistent facts, and automated reasoning. For example, the words “the Sienna” refer to a specific entity when used in my home context, but to an entire family of cars when used in the automobile industry context. And, in pulling together user reviews of its features, the Sienna might be considered good in one user’s context and bad in another user’s context. Characterizing and clustering such contexts of mention saves us from inappropriate use of averaging across mutually inconsistent data sources.

The real power of context, and so far the area of its greatest unrealized potential, is in automated reasoning. Context can be viewed as a theory—a collection of facts (entities and their inter-relationships) and rules—describing the environment of a user or an event. By lifting these facts and rules into a particular (sparsely described) situation, context makes possible inference. Inference is the foundation that makes devices and services look smart, and capable of working with mere clues as to the user’s intentions and preferences. Contextual inference will ultimately give us the technology for making even better recommendations, be they songs, product suggestions, or other search results.

Selected Articles for Context-Aware Computing

In 2007, Jordi Docter, Carlo Alberto Licciardi, and Marco Marchetti surveyed the state of context-aware computing in “The Telecom Industry and Context Awareness.” (login required for full text) They laid down the blueprint for an industry-wide ecosystem of context providers and brokers, enmeshed with content providers and service providers. In 2010, much of what they lamented—such as the lack of “successful implemented context aware services in the market”—has started to change, but, interestingly, along the lines they had suggested in this paper.

In “Timely and Keyword-Based Dynamic Content Selection for Public Displays,” (login required for full text) Fernando Reinaldo Ribeiro and Rui José use the approach of creating a shared context across users through place-centric content integration and summarization, rather than direct modeling and sensing of diverse user contexts. Even their simplistic models of context illustrate the use of context in topic disambiguation.

In “A Contextualized and Personalized Approach for Mobile Search,” (login required for full text) Feng Gui, Malek Adjouadi, and Naphtali Rishe present device-based mechanisms of context capture and a server-side mechanism for context modeling and analysis that supports contextualized expansion of search queries and contextualized ranking of search results. Their work further illustrates the role of context in resolving ambiguous query keywords.

S.M. BaalaMithra and S.M. SominMithraa describe more explicit modeling of the context of a search user, albeit on a per-session basis, in “Context-Aware Automatic Query Refinement Using Indian-Logic Based Ontology.” (login required for full text) Their CAQR algorithm models the various contexts of ambiguous concepts in a context ontology—for instance, explicitly representing taxonomical and other relationships describing the five different senses of the word “apple.” Two other uses of context are well illustrated by CAQR: first, clustering of search results by context, and second, ranking of contexts and re-ranking of search results based on the user’s selection from a ranked listing of contextually clustered results. The paper shows massive improvements in precision and contextualization of search using this algorithm.

Antonis Bikakis and Grigoris Antoniou present an algorithm for the challenging task of integrating possibly inconsistent information from multiple partial contexts accumulating in a distributed fashion. Their article, “Defeasible Contextual Reasoning with Arguments in Ambient Intelligence,” (login required for full text) also illustrates the representation of context as a theory and the use of advanced logics and logic-based systems in computing with context.

The blueprint for an ecosystem of context providers and brokers is falling into place. Starting with contextualized delivery of information, alerts, and advertisements, the opportunities for context-aware computing are as abundant as the places in which technology will become embedded in our lives. By building sensors for gathering context clues and platforms for integrating and serving context, we as engineers can accelerate the development of context-aware applications. In March 2012, IEEE Internet Computing magazine plans to run a special issue on the same topic (Beyond Search), in which we will share further progress on this important theme.

We hope you enjoy reading these articles as much as we did. For further information, take a look at these Related Resources below.

Guest Editor

Pankaj Mehra is a Distinguished Technologist at HP Labs. He’s also a member of IEEE Internet Computing magazine’s editorial board. Contact him at pankaj.mehra@ieee.org.

Related Resources

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1. R. Schmohl and U. Baumgarten, “Distributed Management of Contextual Affinities in Context-Aware Systems,” Proc. 5th Ann. ICST Wireless Internet Conf. (WICON 2010), IEEE Press, 2010.
2. C.A. Schipiura and E.E. Scalabrin, “Application of Web-Business with Ontology Constructed from Database Contextualization,” Proc. 9th Int’l Conf. Computer Supported Cooperative Work in Design, vol. 2, IEEE CS Press, 2005, pp. 1211–1216.
3. Juan Manuel Pérez et al., “Towards a Data Warehouse Contextualized with Web Opinions,” Proc. 2008 IEEE Int’l Conf. e-Business Eng., IEEE CS Press, 2008, pp.697–702.
4. Maria Riaz et al., “Incorporating Semantics-Based Search and Policy-Based Access Control Mechanism in Context Service Delivery,” Proc. 4th Annual ACIS Int’l Conf. Computer and Information Science (ICIS 05), IEEE CS Press, 2005, pp.175–180.
5. Sergio Martin et al., “A Middleware for Mobile and Ubiquitous Learning Ecosystems Based on a Reconfigurable Plug-and-Play Architecture: Application to Mashups,” Proc. 2010 IEEE 24th Int’l Conf. Advanced Information Networking and Applications Workshops, IEEE CS Press, 2010, pp.1–6.
6. Graeme Stevenson et al., “LOC8: A Location Model and Extensible Framework for Programming with Location,” IEEE Pervasive Computing, vol. 9, no. 1, Jan.–Mar. 2010, pp. 28–37.
7. Xuan-Hieu Phan et al., “A Hidden Topic-Based Framework Towards Building Applications with Short Web Documents,” IEEE Trans. Knowledge and Data Eng., preprint, 04 Feb. 2010.
8. Kwang-Eun Ko and Kwee-Bo Sim, “Development of Context Aware System based on Bayesian Network riven Context Reasoning Method and Ontology Context Modeling,” Proc. Int’l Conf. Control, Automation and Systems (ICCAS 08), IEEE Press, 2008, pp. 2309–2313.
9. Lian Yu, Shan Luo, and Arne Glenstrup, “Rough Sets Based Context-Aware Service Discovery Framework,” Proc. 2010 Int’l Conf. Service Sciences, IEEE CS Press, 2010, pp.167–172.
10. Andreas Bulling, Daniel Roggen, and Gerhard Troester, “What’s in the Eyes for Context-Awareness?” IEEE Pervasive Computing, preprint, 03 May. 2010.
11. J.J. Levandoski and M.F. Mokbel, “CareDB: A Context and Preference-Aware Location-Based Database,” Proc. IEEE 26th Int’l Conf. System Data Eng. Workshops (ICDEW), IEEE Press, 2010, pp. 317–320.