ACM Computing Surveys (CSUR)
Understanding and Using Context
Personal and Ubiquitous Computing
Reconfigurable Context-Sensitive Middleware for Pervasive Computing
IEEE Pervasive Computing
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
Supporting Context-Aware Media Recommendations for Smart Phones
IEEE Pervasive Computing
CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications
IEEE Transactions on Software Engineering
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Unsupervised activity recognition using automatically mined common sense
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Activity recognition in the home using a hierarchal framework with object usage data
Journal of Ambient Intelligence and Smart Environments
Context-aware recommendations on mobile services: the m:Ciudad approach
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
XCS for personalizing desktop interfaces
IEEE Transactions on Evolutionary Computation
Preprocessing techniques for context recognition from accelerometer data
Personal and Ubiquitous Computing
Using a Hidden Markov Model for Resident Identification
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
Exploring semantics in activity recognition using context lattices
Journal of Ambient Intelligence and Smart Environments
Hi-index | 0.00 |
The purchase and download of new applications on all types of smartphones and tablet computers has become increasingly popular. On each mobile device, many applications are installed, often resulting in crowded icon-based interfaces. In this paper, we present a framework for the prediction of a user's future mobile application usage behavior. On the mobile device, the framework continuously monitors the user's previous use of applications together with several context parameters such as speed and location. Based on the retrieved information, the framework automatically deduces application usage patterns. These patterns define a correlation between a used application and the monitored context information or between different applications. Furthermore, by combining several context parameters, context profiles are automatically generated. These profiles typically match with real life situations such as 'at home' or 'on the train' and are used to delimit the number of possible patterns, increasing both the positive prediction rate and the scalability of the system. A concept demonstrator for Android OS was developed and the implemented algorithms were evaluated in a detailed simulation setup. It is shown that the developed algorithms perform very well with a true positive rate of up to 90% for the considered evaluation scenarios.