Humans are creatures of habit, often developing a routine for their day-to-day life. We propose a way to identify routine as regularities extracted from the context data of mobile phones. We choose Lecroq et al.'s existing state of the art algorithm as basis for a set of modifications that render it suitable for the task. Our approach searches alignments in sequences of n-tuples of context data, which correspond to the user traces of routine activity. Our key enhancements to this algorithm are exploiting the sequential nature of the data an early maximisation approach. We develop a generator of context-like data to allow us to evaluate our approach. Additionally, we collect and manually annotate a mobile phone context dataset to facilitate the evaluation of our algorithm. The results allow us to validate the concept of our approach.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00944105 |
| Date | 05 February 2014 |
| Creators | Moritz, Rick |
| Publisher | INSA de Rouen |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | fra |
| Detected Language | English |
| Type | PhD thesis |
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