# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 6 Evaluation and Results
### Case Study: Web Service Recognition by Key and Partial Signals  
We assumed that the input patterns corresponded to the ACM shown in Figure 7. Each WS was recognized by the four key and five partial signals, as described in Table 5. Each WS discerned exactly when to execute its operations and with whom it had to interact [30]. Its main objective was the collaborative exchange of messages between the WS in public business processes. Figure 9 depicts an example of a composite service for storing messages on a file server, showing the interaction of the DOTSGeoPhone, BINDService, and WebStoreService services, this comprises their FRs. The user requirement threshold for the composite service was 90%. Table 5 presents the WS after the \(M()\) function was invoked, and the recognition features are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PS** & **KS** & **PS Success Rate** & **KS Success Rate** \\ \hline
2 & 7 & 82 & 18 \\
4 & 5 & 67 & 33 \\
1 & 8 & 91 & 9 \\
7 & 2 & 21 & 79 \\
5 & 4 & 41 & 59 \\
8 & 1 & 11 & 89 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PS and KS recognition success rate results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(WS_{T}\) & **PS** & \(WS_{C}\) & **Run Time (s)** & **SP** \\ \hline
10 & 2 & 180 & 2.13 & 42 \\
50 & 2 & 418 & 3.25 & 56 \\
100 & 2 & 5620 & 427.2 & 402 \\
10 & 4 & 83 & 3.07 & 28 \\
50 & 4 & 137 & 4.23 & 39 \\
100 & 4 & 1827 & 201.3 & 193 \\
10 & 9 & 32 & 1.2 & 13 \\
50 & 9 & 63 & 4.22 & 19 \\
100 & 9 & 731 & 71.2 & 67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Runtime results using PS. The recognition strategy set the first four QoSs to be recognized by key signals and the rest as partials. Table 6 presents the \(\Delta KS\) values for each service. The \(\Delta PS\) was calculated from their average, as exemplified in Figure 6.  
Based on recognition by the function \(M()\) and Equations (4) and (5), the method worked as follows:  
1. The QoS matrix was derived from Equation (2). It allowed for defining the values to be used by the recognition modules (\(\Gamma\)). \[QoS= \begin{bmatrix}266.83&36&0.9&37&60&89&69&15.91&7\\ 261&36&0.9&37&60&89&69&13.67&12\\ 102&90&18.6&97&73&78&80&1&92\\ 107.4&85&19&95&73&78&80&0.8&92\\ 251.07&56&52&58&73&67&80&133.43&31\end{bmatrix} \] 2. Figure A1 shows the two BINDServices which were recognized as two candidates. Their four signals were active and with a respective ranking. However, the service with the best score was listed first. This process resulted in a signal from service 2 (response time = \(QoS_{1,0}=261\)), which had a response time value which was more appropriate for the requested quality criteria. 3. Figure A2 shows a single service with partial recognition, which allows the system to easily and quickly choose the WS. Partial signals were active, and the service had the highest-ranking value. In this case, the first DOTSGeoPhone service was discarded, as its value of \(QoS_{2,8}=1\) was outside the average range. Key signals outside the \(\Delta\) value did not recognize the services, and the system performed recognition using partial signals. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Service Name** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline BINDService & KS = 300 & KS = 36 & KS = 0.8 & KS = 36 & PS & PS & PS & PS & PS \\ \hline DOTSGeoPhone & KS = 106 & KS = 90 & KS = 187 & KS = 100 & PS & PS & PS & PS & PS \\ \hline WebStoreService & KS = 300 & KS = 60 & KS = 60 & KS = 60 & PS & PS & PS & PS & PS \\ \hline \end{tabular} (1) Response Time, (2) Availability, (3) Throughput, (4) Success, (5) Reliability, (6) Compliance, (7) Best Practices, (8) Latency and (9)Documentation.  
\end{table} 
Table 6: Case study web services description of the type of recognition performed.