Name
Abstract | ||
|---|---|---|
We develop a Markov state transition model of smartphone screen use. We collected use traces from real-world users during a 3-month naturalistic deployment via an app-store. These traces were used to develop an analytical model which can be used to probabilistically model or predict, at runtime, how a user interacts with their mobile phone, and for how long. Unlike classification-driven machine learning approaches, our analytical model can be interrogated under unlimited conditions, making it suitable for a wide range of applications including more realistic automated testing and improving operating system management of resources.
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Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2971648.2971669 | UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Heidelberg
Germany
September, 2016 |
Keywords | Field | DocType |
Markov chains, smartphone, model, prediction | Software deployment,Computer science,Markov chain,Human–computer interaction,Mobile phone | Conference |
ISBN | Citations | PageRank |
978-1-4503-4461-6 | 8 | 0.57 |
References | Authors | |
25 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis Kostakos | 1 | 1718 | 138.50 |
| Denzil Ferreira | 2 | 768 | 49.89 |
| Goncalves, J. | 3 | 404 | 42.24 |
| Simo Hosio | 4 | 668 | 53.04 |