## 4 Results
### SLO/SLA and the Obtained User-Engagement Score Correlation  
Having the user-engagement score calculated, the service level objectives and agreements definition procedure gets improved. By calculating the appropriate user-engagement scores for educational or e-business project components, we can filter these components according to particular criteria, which will let the web project parties find the aggregated user-engagement values (mean, median, percentiles) only for those units that interest us most, and compare them with other groups. Also, based on multiple entities of the user-engagement scores, we can find the overall scores using statistical and mathematical functions and monitor them over time. Based on the values obtained from these computations and SLOs/SLAs data, we propose a correlation matrix to show how the user-engagement score can affect them and the technical equipment, and facilitate its assessment. Thus, applying technical condition assessment is necessary to increase the availability of an e-commerce or e-learning project, especially when there is a high demand for it. Using the SLO and the SLA allows us to assess the infrastructure condition and make objective decisions about the application architecture. We should take into account that the SLO setting process requires a preliminary system and potential infrastructure risks understanding as well as performing user-behavior analysis. The following is a contingency table that helps to quantify user-engagement score intervals frequency with data about the SLO/SLAs adherence. The user-engagement score can be generic for the whole platform, as well as applying for individual web project components. Based on the contingency table data, we can find the association between a user and the online-resource interaction, how positive it has been, and what might be improved. For example, if the SLO for availability is defined, which is directly related to the technical equipment condition, it is profoundly observable how it associates with a certain web-resource user-engagement or interaction. It is thus possible to display the cases when the SLO or the SLA has been high (Table 2), even though the user-engagement has not been or vice versa. The relative frequencies usage is also recommended for a clear contingency table results interpretation. We developed a 3 \(\times\) 3 matrix based on the SLO/SLA compliance levels and user-engagement score. The levels (low, moderate, high) are custom for each web project. In our case we define the following ranges (0-0.05), (0.05-0.65), (0.65-1) as low, moderate and high, correspondingly. The SLO/SLA levels depend on their definition documents. For instance, the current published target for Google Compute Engine availability is 99.95% availability [35]. If this target is met, we define the SLO/SLA level as high (Table 3). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**User-Engagement Standardized Score**} \\ \hline
**SLO/SLA** & **0–0.25** & **0.25–0.5** & **0.5–0.75** & **0.75–0.95** & **0.95–1** & **Total** \\ \hline
0–25\% & X\({}_{11}\) & X\({}_{12}\) & X\({}_{13}\) & X\({}_{14}\) & X\({}_{15}\) & X\({}_{1+}\) \\
25–50\% & X\({}_{21}\) & X\({}_{22}\) & X\({}_{23}\) & X\({}_{24}\) & X\({}_{25}\) & X\({}_{2+}\) \\
50–75\% & X\({}_{31}\) & X\({}_{32}\) & X\({}_{33}\) & X\({}_{34}\) & X\({}_{35}\) & X\({}_{3+}\) \\
75–95\% & X\({}_{41}\) & X\({}_{42}\) & X\({}_{43}\) & X\({}_{44}\) & X\({}_{45}\) & X\({}_{4+}\) \\
95–100\% & X\({}_{51}\) & X\({}_{52}\) & X\({}_{53}\) & X\({}_{54}\) & X\({}_{55}\) & X\({}_{5+}\) \\ Total & X\({}_{+1}\) & X\({}_{+2}\) & X\({}_{+3}\) & X\({}_{+4}\) & X\({}_{+5}\) & X \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contingency table for the SLO/SLA data and user-engagement scores correlation. Accordingly, the higher the user-engagement score level, the more active users interact with the platform, the more they expect this platform to meet the SLAs.