A Survey of Context-Aware Mobile Computing Research

By Guanling Chen, David F. Kotz. 2000

2,289 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a9d0b687da10c70bbbcc73d3657c96d92af6ada2

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Citations in the corpus

A Context Modeling Survey (2004, background): “FUNDAMENTALS In the literature several definitions of the term context can be found [36, 35, 32, 37, 10, 16, 41].”
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, background): “Chen and Kotz [22] added time as a fourth context category.”
Redpin - adaptive, zero-configuration indoor localization through user collaboration (2008, background): “Luckily, however, surveys of the most prominent challenges and issues in ubiquitous computing have shown that it is sufficient to localize a user within room-level accuracy for almost all applications [1, 3, 6, 12, 15, 30].”
The Horus WLAN location determination system (2005, unknown): “The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.”
Understanding mobile contexts (2003, background): “As a result, the viewpoint of the end-users has been ignored (see [9]).”
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material (2017, methodology): “They have used ML to infer people’s states, like their interruptablity [31], their routines [14], and other important contextual information [11], which can mediate computer interaction.”
WLAN location determination via clustering and probability distributions (2003, background): “This in turn fuels the need to determine user location, with which the system can provide location-specific information and services [6].”
Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems (2019, background): “Scheduling Assistant represents a type of system that is passive/assistive and casual (similar to “passive context-awareness” from [9]).”
Context Aware Computing for The Internet of Things: A Survey (2013, background): “During the last two decades, researchers and engineers have developed a signiﬁcant amount of prototypes, systems, and solutions using context-aware computing techniques.”
E-learning, M-learning and D-learning: Conceptual definition and comparative analysis (2018, background): “According to Chen and Kotz (2000), there are four categories of mobile context, namely, computing context, user context, physical context, and the time context.”
Recommender Systems (2016, unknown): “An early survey of context-aware computing research for mobile systems may be found in [147].”
Is Context-Aware Computing Taking Control away from the User? Three Levels of Interactivity Examined (2003, methodology): “Personalization is where applications let the user specify his own settings for how the application should behave in a given situation; passive context-awareness presents updated context or sensor information to the user but lets the user decide how to change the application behavior, where active c”
Context-aware systems: A literature review and classification (2009, background): “Chen and Kotz (2000) surveyed the literature related with context-aware computing in mobile computing.”
Smart health: A context-aware health paradigm within smart cities (2014, unknown): “, applications and services that automatically adapt to discovered context) by changing the application’s and services’ behavior [2].”
A data-oriented survey of context models (2007, background): “Interesting surveys on context-aware systems and models have already been presented in [5, 14, 39, 45]; we contribute with a review on recent evolutions and new systems for context modeling, and challenge each model with respect to the problem of context-aware data tailoring.”