# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 1 Introduction  
Web service composition (WSC) is defined as a logical combination of web services (WSs), which provides a level of collaboration and data exchange between them in a synchronized manner in order to satisfy a functional requirement (FR). The service constitutes a component that exposes its functionalities through interfaces under the specific language of the definition [1]. In combination with other services, these functionalities are those that satisfy specific requirements. The composition task depends on placing logically arranged services and involves aspects defined in its non-functional requirements (NFRs), which constitute quality of service (QoS) criteria to select WSs [2]. The FRs establish which services will be in the WSC, and the NRFs establish which QoS criteria those services will have to satisfy. FRs provide the identifiers and interfaces of the services and how they will exchange messages. Usually, WS deployment is performed under cloud providers, such that attention should be paid to the resources consumed by each of the distributed providers. Consequently, user interaction and resource consumption are essential factorsto consider during composition. The exponential growth in services with different QoS levels over providers [3] with the same functionality poses challenges in selecting a WS, as there may be services with the same functionalities in different providers, but with different QoS values [4]. The advantages of cloud environments can create inherent problems generated by vendors whose deployment of WSs on different nodes [5] can generate a variety of QoS criteria. Thus, non-functional requirements must be analyzed with caution, such as the ability to establish agreements for the provisioning and acquisition of computational resources (static or dynamic negotiations), if the composition requires it [6]. Each service exchanges messages with its peers through connectors of an atomic service (a simple WS). In turn, a complex or composite service (joining several WSs) is developed, which interacts through some functional logic. Its scalability and growth levels mainly depend on the flexibility of the selected services, such that, if a service is independent of constant QoS criteria, these can be used later by another composite service [6]. The web service selection problem has been described in [7], where the emphasis was placed on the exponential growth of services with different QoS for the same functionality, leading to combinatorial computational costs for the search and selection processes. Our research constructed a computational strategy to discover and select WSs within the pattern-recognition domain. The strategy builds from modeling services and their characteristics as code units to be recognized by a pattern system, dynamically created by the service composition model. Pattern recognition offers methods and algorithms for the classification of objects based on the analysis of their attributes. Our classification approach models the system using the concept of learning--this refers to algorithms capable of solving the unknowns in the patterns. This solution allows for eliminating the noise in the information, increasing the reliability of its treatment. Learning can occur in different ways, as has been reported in the literature. Within these approaches, Ar2p [8] uses supervised learning through a strategy that offers an overview of pattern modeling based on the systematic functioning of the brain. Our proposed method uses a different approach than Ar2p, at the level of the recognition strategy: the original Ar2p recognizes entities using an interval model to classify the pattern previously loaded in the system. On the other side, our approach dynamically analyzes the classification pattern and decomposes it for later analysis by the different recognizer modules, dynamically created given the user's requirements.