Name
Abstract | ||
|---|---|---|
Context awareness and prediction are important for pervasive computing systems. The recently developed theory of context spaces addresses problems related to sensor data uncertainty and high-level situation reasoning. This paper proposes and discusses componentized context prediction algorithms and thus extends the context spaces theory. This paper focuses on two questions: how to plug-in appropriate context prediction techniques, including Markov chains, Bayesian reasoning and sequence predictors, to the context spaces theory and how to estimate the efficiency of those techniques. The paper also proposes and presents a testbed for testing a variety of context prediction methods. The results and ongoing implementation are also discussed. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04190-7_2 | NEW2AN |
Keywords | Field | DocType |
data uncertainty,context spaces addresses problem,plug-in appropriate context prediction,predicting run-time context,high-level situation reasoning,bayesian reasoning,markov chain,context prediction method,context awareness,extending context spaces theory,discusses componentized context prediction,context spaces theory,branch prediction,pervasive computing,bayesian network,markov model,neural network,development theory | Data science,Bayesian inference,Computer science,Markov model,Markov chain,Testbed,Context awareness,Context model,Bayesian network,Artificial intelligence,Ubiquitous computing,Machine learning | Conference |
Volume | ISSN | Citations |
5764 | 0302-9743 | 9 |
PageRank | References | Authors |
0.62 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrey Boytsov | 1 | 71 | 5.82 |
| Arkady Zaslavsky | 2 | 1133 | 81.03 |
| Kåre Synnes | 3 | 192 | 25.16 |