Name
Abstract | ||
|---|---|---|
Much research on context quality in context-aware systems divides into two strands: (1) the qualitative identification of quality measures and (2) the use of uncertain reasoning techniques. In this paper, we combine these two strands, exploring the problem of how to identify and propagate quality through the different context layers in order to support the context reasoning process. We present a generalised, structured context quality model that supports aggregation of quality from sensor up to situation level. Our model supports reasoning processes that explicitly aggregate context quality, by enabling the identification and quantification of appropriate quality parameters. We demonstrate the efficacy of our model using an experimental sensor data set, gaining a significant improvement in situation recognition for our voting based reasoning algorithm. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-04559-2_6 | QuaCon |
Keywords | Field | DocType |
appropriate quality parameter,context quality,structured context quality model,aggregate context quality,context reasoning process,uncertain context,propagate quality,different context layer,reasoning process,reasoning algorithm,quality measure,transparent reasoning | Reasoning algorithm,Data mining,Pervasive systems,Voting,Computer science,Context model,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | ISBN |
5786 | 0302-9743 | 3-642-04558-8 |
Citations | PageRank | References |
10 | 0.74 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Susan McKeever | 1 | 75 | 2.72 |
| Juan Ye | 2 | 125 | 9.82 |
| Lorcan Coyle | 3 | 612 | 31.96 |
| Simon Dobson | 4 | 1125 | 60.75 |