Collecting, monitoring, and analyzing data automatically by well instrumented systems is frequently motivated by human decision-making. However, the same need occurs when system software decisions are to be justified. Compiler optimization or storage management requires several decisions which result in more or less resource consumption, be it energy, memory, or runtime. A magnitude of system data can be collected in order to base decisions of compilers or the operating system on empirical analysis. The challenge of large-scale data is aggravated if system data of small and often mobile systems are collected and analyzed. In contrast to the large data volume, the mobile devices offer only very limited storage and computing capacity. Moreover, if analysis results are put to use at the operating system, the real-time response is at the system level, not on the level of human reaction time. In this paper, small and most often mobile systems (i.e., ubiquitous systems) are instrumented for the collection of system call data. It is investigated whether the sequence and the structure of system calls are to be taken into account by the learning method, or not. A structural learning method, Conditional Random Fields (CRF), is applied using different internal optimization algorithms and feature mappings. Implementing CRF in a massively parallel way using general purpose graphic processor units (GPGPU) points at future ubiquitous systems.