## 5 Results and Discussion  
We tested the effectiveness of the proposed CATIM verifying its interactivity by measuring two execution times: the reasoning time and the turnaround time. The reasoning time we define as the time the back-end takes to load all required information into memory and then to send the recommended response to front-end. The turnaround time we define as the elapsed time from which the user requests information to the time in which technical assets are rendered on AR user interface. During this time, users must wait for CATIM and thus cannot perform any interaction. Reasoning and turnaround time were measured without considering network communication time, as all assets are stored in the back-end which runs on the local network. We recorded reasoning and turnaround time seven times for each AR device. The mean reasoning time for the 28 (7 trial \(\times\) 4 devices) measures was 18.3 ms. The mean turnaround times for the four devices are reported in Figure 7 and suggest that CATIM can support highly-interactive user experiences. We observed that the ranking of times measured for the smartphones match that of the AnTuTu benchmark, as expected. Finally, we observed that the standard deviations are higher on smartphones that have many external processes and thus their execution times may vary. These results suggest that CATIM can support interactive system performance rates, showing acceptable performance in terms of turnaround time over a series of tested devices. While we used  
Figure 6: (**a**) Small GUI layout (shown with HoloLens) on the left; and (**b**) Large GUI layout (screenshot captured from smartphone) on the right. Figure 7: Results about CATIM turnaround times for each device used in the test. The times (all below 1000 ms) reported allow an interactive behavior of the system. a reduced context dataset for this work, the system is designed in a way to maintain interactive processing speeds with larger datasets. The properties of the recommendation system allow it to be scalable, eventually dividing the ontologies into knowledge subsets, while the visualization engine does not depend in any way on the system size. For this purpose, a valuable attribute of the system would be the possibility of automatically populating the ontology with a set of technical manuals, by taking them from large datasets (e.g., IFixit), often written in authoring-oriented standards, like oManual [26], DITA [27] or DocBook [28]. As to the context-awareness capability, we tested all the possible context vectors (i.e., activity/operator/environment attribute combination) verifying which technical assets are recommended by CATIM. The results were consistent with what we would predict according to the concept covering rules for the current context. As expected, the recommendation process removes all the asset types which create contradictions with the current context. Some of the responses reported more than one compatible asset, suggested in decreasing order of importance, thus only the first asset was automatically shown, while the others were hidden (but still manually selectable by the operator if desired). In the case of an expert user, the number of technical information assets suggested is lower, as s/he is expected to need less information to accomplish familiar and common tasks. Comparing CATIM with other context-aware AR systems presented in the literature, we can say that CATIM can: (i) manage all the possible types of visual assets; (ii) consider the 'people' factor; and (iii) implement user-defined rules for the management. This last aspect is crucial because when established standard about the visualization of technical documentation in AR will be available [29], it would be possible to integrate them in the reasoner for the right choice of technical assets. By taking a closer look at the results for the context with a novice operator, we can observe that the world-fixed assets are always returned by the system in a specific order (CAD > Video > Photo).