Industrial Scenario: We limit our scope in this poster paper to smart welding manufacturing at Bosch. StatsOnto aims to help the engineers at Bosch to gain insights from the data collected from the welding production, and to monitor quality of the welding operations.

R1 Procedure Orientation: We use an example for data denoising (Fig. 2) for demonstrating the procedure orientation. Given the input of Q-Value Array, the pipeline first extracts its trend by calculating the median value with sliding window, then calculates the scattering by the difference between the trend and Q-Value. The points with large scattering (large deviation from the trend) are detected as the outliers. This shows StatsOnto is capable of representing the procedure of statistical analytics.

R2 Transparency: We organised a workshop at Bosch and collected 28 reports from experts of different backgrounds. The users first perform statistical analytical tasks with or without our method, and then answer several single-selected questions, their correctness of these questions reflect their understanding towards these analytics methods.
R3 Knowledge Coverage: We select two example CQs in SPARQL from two aspects: knowledge query (Fig. 2c), analysis procedure query (Fig. 2d).

R4 Purpose Coverage: After extensive dissuasion among the expert users in the workshop, we categorised most statistical analytical tasks in our project into the four types of purposes, and find that, according to our empirical cases, most of categories can be covered (above 80%).