Beyond Memoryless Distributions: Model Checking Semi-Markov Chains
PAPM-PROBMIV '01 Proceedings of the Joint International Workshop on Process Algebra and Probabilistic Methods, Performance Modeling and Verification
Model-Checking Algorithms for Continuous-Time Markov Chains
IEEE Transactions on Software Engineering
Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data
IEEE Transactions on Mobile Computing
Online Sequential Prediction via Incremental Parsing: The Active LeZi Algorithm
IEEE Intelligent Systems
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Adaptable Pervasive Flows - An Emerging Technology for Pervasive Adaptation
SASOW '08 Proceedings of the 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops
Predicting the location of mobile users: a machine learning approach
Proceedings of the 2009 international conference on Pervasive services
Prediction in wireless networks by Markov Chains
IEEE Wireless Communications
SFM'07 Proceedings of the 7th international conference on Formal methods for performance evaluation
Ubiquitous Advertising: The Killer Application for the 21st Century
IEEE Pervasive Computing
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Ubiquitous systems need to determine the context of humans to deliver the right services at the right time. As the needs of humans are often coupled to their future context, the ability to predict relevant changes in a user's context is a key factor for providing intelligence and proactivity. Current context prediction systems only allow applications to query for the next user context (e.g. the user's next location). This severely limits the benefit of context prediction since these approaches cannot answer more expressive time-dependent queries (e.g. will the user enter location X within the next 10 minutes?). Neither can they handle predictions of multi-dimensional context (e.g. activity and location). We propose PreCon, a new approach to predicting multi-dimensional context. PreCon improves query expressiveness, providing clear formal semantics by applying stochastic model checking methods. PreCon is composed of three major parts: a stochastic model to represent context changes, an expressive temporal-logic query language, and stochastic algorithms for predicting context. In our evaluations, we apply PreCon to real context traces from the domain of healthcare and analyse the performance using well-known metrics from information retrieval. We show that PreCon reaches an F-score (combined precision and recall) of about 0.9 which indicates a very good performance.