Context is key

By Joëlle Coutaz, James L. Crowley, S. Dobson, David Garlan. CACM, 2005

584 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/fae278fb95bf02a797e696ad40b459c6f5bfa971

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Pavel Shvaiko
2. Jérôme Euzenat
3. James L. Crowley
4. Carlo Curino
5. Oliver Brdiczka
6. Michel Vacher
7. Pedro Chahuara
8. François Portet

In Computer Vision and Pattern Recognition

1. Billur Barshan
2. Stephen J. Preece
3. David Howard
4. John Yannis Goulermas
5. Laurence P. J. Kenney
6. Kerem Altun
7. Benjamin Lecouteux
8. James L. Crowley

In Mechanical Engineering

1. Fiona Fui-Hoon Nah
2. Keng Leng Siau
3. Hong Sheng

Citations in the corpus

• A survey of autonomic communications (2006, background): “It is widely recognized that managing the structures of context is a signi.cant challenge [Coutaz et al. 2005].”
• Ontology Matching (2007, background): “If one wants to design flexible and smart ambient computing applications, it is useful to take advantage of the ontologies of these various devices, those of sensors available in the environments and their capabilities (Coutaz et al. 2005).”
• The Sweet-Home speech and multimodal corpus for home automation interaction (2014, background): “…increased interest in automatic human activity recognition from sensors just as well in Ambient Intelligence domain for context-aware interaction (Coutaz et al., 2005) as in Ambient Assisted Living domain for behaviour monitoring in health applications (Fleury et al., 2010). ha l-0 09 53 00 6, v…”
• A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data (2009, background): “In addition to health-related applications, portable systems, which can accurately identify the activity of the user, have the potential to play a fundamental role in a ubiquitous computing scenario [9], [10].”
• The Smart Home Concept : our immediate future (2006, background): “Figure 3 : Necessary abstraction levels for context awareness system [1]”
• Activity classification using realistic data from wearable sensors (2006, background): “Context sensing and use of context information is an important part of the ubiquitous computing scenario [23]–[25].”
• Detecting Human Behavior Models From Multimodal Observation in a Smart Home (2009, background): “Context is the key for interaction without distraction [15].”
• An Experimental Study on Ubiquitous commerce Adoption: Impact of Personalization and Privacy Concerns (2008, background): “Therefore, context is key in u-commerce applications (Coutaz et al., 2005).”
• Activity identification using body-mounted sensors—a review of classification techniques (2009, background): “In addition to health-related applications, activity profiling systems could play a fundamental role in ubiquitous computing scenarios (Coutaz et al 2005; Streitz and Nixon 2005).”
• A data-oriented survey of context models (2007, background): “In [18] the authors describe some principles to be considered when implementing context-aware applications; the context model informally presented can be classified into the E category.”