By Stephen L. Campbell, Jean-Philippe Chancelier, Ramine Nikoukhah
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Extra info for Mobility, Data Mining, And Privacy - Geographic Knowledge Discovery
Typically, the required precision cannot be achieved in a simple (atomic) pattern but rather in a compound pattern built from sub-patterns, which, in turn, may also be compound. Compound patterns result from decomposing the overall behaviour into parts, representing these partial behaviours by sub-patterns, and, ﬁnally, bringing the subpatterns together into an overall pattern. The decomposition is required because the movement behaviour is not uniform throughout the reference set. e. involves relation search and comparison questions.
The relative time references would in this case be the time counts from the beginning of each movement, possibly, standardised in the way of dividing them by the duration of the movement. As we have noted, the properties of time moments and intervals may vary, and this variation may have signiﬁcant inﬂuence on movements. For example, the movements of people on weekdays notably differ from the movements on weekends; moreover, the movements on Fridays differ from those on Mondays and the movements on Saturdays differ from those on Sundays.
E. Tufte. Visual Display of Quantitative Information. Graphics Press, 1983. Chapter 2 Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process M. Wachowicz, A. Ligtenberg, C. Renso, and S. 1 Introduction The proliferation of mobile technologies for ‘always-on’ at ‘any-time’ and ‘anyplace’ has facilitated the generation of huge volume of positioning data sets containing information about the location and the movement of entities through the geographic environment.