# L-rCBF: Learning-Based Key-Value Data Structure for Dynamic Data Processing
## 5 Performance Evaluation
### Performance Comparison of rCBF, L-FBF, and L-rCBF
However, if insertions and deletions are repeated for dynamic data, the performance gap of the search failure rates between the L-rCBF and L-FBF with the same model would be reduced owing to an increase in the number of conflict cells in the V-FBF. Furthermore, because of the significantly superior deletion performance of the L-rCBFs compared with that of the L-FBFs, as shown in Table 2, the L-rCBFs are more appropriate than L-FBFs for dynamic data processing. In addition, when comparing the L-FBF\({}_{1}\) to L-FBF\({}_{2}\) or the L-rCBF\({}_{1}\) to L-rCBF\({}_{2}\), in terms of the search and deletion performances, each structure using Model\({}_{2}\) outperforms each structure using Model\({}_{1}\), despite Model\({}_{2}\) requiring more memory than Model\({}_{1}\), because of the higher accuracy of Model\({}_{2}\). For static data, because insertions and deletions are infrequent and searches are primarily performed, using the L-FBF with an improved search performance is more efficient than using a single rCBF. Especially if the FBF for dynamic data is replaced with that for static data in the L-FBF, the search performance of the L-FBF improves. Table 4 compares the _search failure_ rates of the rCBF, L-FBF\({}_{2}\) with the FBF for dynamic data, and L-FBF\({}_{2}\) with the FBF for static data when using the same amount of memory. Because the number of conflict cells in the FBF for static data is less than that for dynamic data, using the FBF for static data can reduce the number of INDETs included in set \(S\). Hence, the L-FBF for static data is more efficient than a single rCBF and the L-FBF for dynamic data when insertions and deletions are infrequent. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **rCBF** & **L-FBF\({}_{1}\)** & **L-FBF\({}_{2}\)** & **L-rCBF\({}_{1}\)** & **L-rCBF\({}_{2}\)** \\ \hline UNDEL-FP & 0 & 0 & 0 & 0.014 & 0.014 \\ UNDEL-C & 0.030 & 0.098 & 0.060 & 0.003 & 0 \\ \hline Total & 0.030 & 0.098 & 0.060 & 0.016 & 0.014 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of undeletable rates when using same amount of memory (%). Additionally, we compare the L-rCBFs (i.e., L-rCBF\({}_{1}\) and L-rCBF\({}_{2}\)) to two L-FBFs (i.e., L-FBF\({}_{3}\) and L-FBF\({}_{4}\)), which are identical to the L-FBF\({}_{1}\) and L-FBF\({}_{2}\) except for the V-FBFs, and the \(\alpha_{v}\) of the V-FBFs have the same values as those of the V-rCBFs in the L-rCBFs: 8 and 8.42, respectively. Table 5 compares the _undeletable_ rates when using a verification structure with the same \(\alpha_{v}\). The undeletable rates of the L-rCBF\({}_{1}\) and L-rCBF\({}_{2}\) are reduced by 98.73% and 98.20%, respectively, compared with those of the L-FBF\({}_{3}\) and L-FBF\({}_{4}\), respectively. In terms of the search performance, if both an L-FBF and L-rCBF with the same model possess the same \(\alpha_{b}\) and \(\alpha_{v}\) values, the structures have the same search failure rates. Hence, the search failure rates of the L-rCBF\({}_{1}\) and L-FBF\({}_{3}\) are the same, and those of the L-rCBF\({}_{2}\) and L-FBF\({}_{4}\) are also the same. However, if insertions and deletions are repeated, the search failure rates of the L-rCBFs would be better than those of the L-FBFs.