# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 7 Discussion
Additionally, selecting a threshold for the composite service may cause the results to vary, according to their solution paths (i.e., the higher the threshold, the more restrictive the system is in selecting its services). Figure 10 shows how Ar_WSDS can adapt either of its two recognition strategies to select services, with partial signal recognition offering the best results, as mentioned above. In the case study, WSs with a variable number of replicates were analyzed using two selection approaches--the first defined by OAEQoS and the second by GDSA--and both algorithms used input features from our approach. WS composition was performed sequentially from \(WS_{1}\) to \(WS_{9}\), allowing the system to have flexibility in selecting the service for each node on the ACM. Tests were performed on the expected (effective) services, and our approach was compared with two existing approaches. Table 8 shows how Ar_WSDS achieved the expected services in its two recognition strategies, compared to the other two approaches; however, there were two cases where our approach recognized another service. This situation arose because the system analyzed all the selection possibilities, and the result of the comparison was performed based on the user's experience, such that the effective WS results may be different. Figure 12 demonstrates the effectiveness of our approach, with GDSA producing more errors in the selection process as its solution strategy performed generic searches without considering any improvement idea that offered acceptable results to the user. Therefore, it is essential for the service selection process to have solution strategies that explore the deep analysis of QoS parameters considering their type. The system must analyze them in comparison to all the services that are similar in their functionality. OAEQoS produced acceptable results, and its capacity to retain updated QoS parameters was notable. However, the implementation of this approach necessitates the incorporation of additional routines to analyze more QoS parameters. Likewise, the update process was performed in remote invocations, which can improve the algorithm's performance, and its analysis became  
Figure 11: KS and PS signal recognition comparison. static to the extent that the system was only based on pre-defined QoS parameters and only defined a selection strategy. In contrast, Ar_WSDS, which can dynamically create recognition modules for any QoS parameter and evaluate their thresholds on the basis of reaching the QoS threshold of the composite service, allows the user to obtain better results for each service involved in the composition. Table 11 shows the ranking results of the WSs. We can appreciate that the two strategies of Ar_WSDS offer a more appropriate selection, compared to the other two approaches. A slight ranking difference existed in key vs. partial recognition, as the latter had a more extensive search space. OAEQoS presented differences under a larger number of WSs as, in its selection process, it must choose more services, excluding some of the recognition. GDSA, although its selection process is more extensive than the other approaches, showed significant differences in its ranking process, compared to Ar_WSDS. This approach selected the vast majority of services when its QoS parameters had little variability.