# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 8 Conclusions  
Service discovery and selection are complex because of the availability of resources at either the design or execution stage, and their behavior can be uncertain and dynamic. Currently, research concerning service composition faces challenges underlying the basic idea of how to search and select a service in a time-efficient way. Furthermore, the dynamic environment in the cloud has led to the specification of non-functional requirements to be dynamic, and therefore a composition model can change from the design phase to its execution. We constructed a system for WS discovery and selection based on the systemic operation of the brain considering patterns. Its process uses two strategies: key and partial signals. This recognition feature offers a method to split the problem into more straightforward jobs. Differently from the Ar2p framework, Ar_WSDS changes the condition of the key and partial signals to adjust to a WS's characteristics. Although Ar2p establishes the two recognition strategies, it is indispensable to adjust its concept in the QoS field for the case of services. Our approach can recognize multiple patterns and divide them into sub-patterns (QoS criteria) to be recognized by the strategy selected by the software developer. Our approach can recognize multiple patterns and divide them into sub-patterns to be recognized by the strategy selected by the software developer. Ar2p operates by giving priority to the key signals. Conversely, we highlight that Ar_WSDS dynamically adapts to  
Figure 12: Error rate in the selection process according to the various algorithms. the recognition type for each pattern, and the system selects the services established both in the ACM and for each WS (\(\Delta\)). As a highlighted contribution, we developed Ar_WSDS based on the concept of recognizing a set of patterns that satisfy dynamic quality conditions, and the recognition strategies were updated to establish a deeper level of analysis on the QoS parameters. The dynamic recognition module creation, based on user requirements, provides an essential advantage for developers who wish to use this approach, as they can customize their searches based on the desired requirement or refine them to obtain better results. The operating principle of recognition strategy-based Ar_WSDS offers an approach to divide the composition selection problem into functional and descriptive tasks. Researchers can quickly adapt its solution concept to other search models needing a wide variety of selection criteria. In this work, we formalized WSs and the composition model from a mathematical perspective, allowing for the interpretation and systematization of the system in a modular architecture, thus facilitating its scalability for the addition of new QoS parameters. QoS has a dynamic nature of values due to the information supplied by the providers, so Ar_WSDS includes a module to assemble the new data without the need to alter its base programming. According to the tests performed, KS recognition provided better performance than PS recognition, as the former limits the number of candidate WSs and, so, the search space is much smaller. However, the PS strategy was more versatile when selecting WSs, as it offers a much more comprehensive range of PS. The recognition success rate varied with the number of constraints imposed on the model and the quality threshold placed on the composition. System performance can be affected when there are similarities in the QoS values of each WSs because the system would have to select all those services within the SP, and in this case, the KS and PS recognition could generate the same results. According to the results, Ar_WSDS achieved excellent results in discovering and selecting WSs, compared to other existing approaches. Its strategy allows users to add more recognizers by interpreting the desired QoS parameter quickly. Overall, the proposed approach is much more effective in the selection and ranking of WSs.