# SMM: Leveraging Metadata for Contextually Salient Multi-Variate Motif Discovery
## 5 Experimental Evaluation
### Results for the Mocap and Energy Datasets  
#### 5.5.1 Accuracy  
InTable 3, before presenting the detailed accuracy results--from Tables 4-6--, we present a set of representative results, comparing accuracies for the SMM-s and MPC algorithms, on a sample scenario (one motif extracted from each series and inserted into 10 random walk time series) based on the MoCap dataset. We use the bold fonts to highlight the best results among the alternatives. As we see in this table, SMM-s leads to a significantly higher precision and overall higher accuracy values. The table also shows that SMM-based approaches are significantly more robust (to instance overlap bounds) than MPC. This means that, as we are targeting motifs that consist of more complete sub-sequences, the proposed SMM technique can provide robust accuracies, while the accuracies of MPC drop as the lower bound gets higher. **Default Scenario. ** We start presenting the detailed accuracy results by considering the default scenario in which we have a pattern composed of the same repeated action, or event, happening at different speeds. The results are presented in Table 4. As we can see in the table, under the default settings and under a wide range of random walk and instance overlap thresholds, SMM provides significantly higher accuracies than its competitor. Figure 9: Sample motifs identified by the proposed SMM approach and a competitor (MPC [25]). The motifs extracted by SMM are contextually meaningful, whereas MPC’s motifs (obtained without considering the metadata associated with the time series) are noisy. (**a**) Sample SMM motif—note that the repeating pattern is detected on sensors at the same (right) side of the body. (**b**) Corresponding motif identified by a competitor—some sensors included in the motif include those that are irrelevant to the action; the motif also misses one recurring instance. \begin{table} \begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score MoCap (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-9}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.61** & 0.45 & **0.59** & 0.32 & **0.56** & 0.33 & **0.53** & 0.33 \\
75\% & **0.65** & 0.45 & **0.64** & 0.28 & **0.61** & 0.26 & **0.56** & 0.25 \\
90\% & **0.71** & 0.43 & **0.69** & 0.22 & **0.66** & 0.22 & **0.62** & 0.21 \\
100\% & **0.68** & 0.31 & **0.65** & 0.17 & **0.62** & 0.16 & **0.58** & 0.15 \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{F-Score Energy (I Motif \(k=10\), \(f=50\)-100\%)} \\ \cline{2-9}
**Instances** & \multicolumn{2}{c}{**RW 10\%**} & \multicolumn{2}{c}{**RW 50\%**} & \multicolumn{2}{c}{**RW 75\%**} & \multicolumn{2}{c}{**RW 100\%**} \\ \cline{2-9}
**Overlap** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** & **SMM-s** & **MPC** \\ \hline
50\% & **0.59** & 0.45 & **0.55** & 0.37 & **0.50** & 0.33 & **0.46** & 0.32 \\
75\% & **0.62** & 0.42 & **0.56** & 0.28 & **0.52** & 0.25 & **0.48** & 0.25 \\
90\% & **0.67** & 0.32 & **0.61** & 0.24 & **0.57** & 0.22 & **0.51** & 0.23 \\
100\% & **0.62** & 0.24 & **0.55** & 0.19 & **0.52** & 0.17 & **0.47** & 0.18 \\ \hline \hline \end{tabular}