# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 2 Background and Related Work
### Pattern Recognition  
Pattern recognition methods have been extensively studied [17; 18], where the related works have mainly targeted selection methods based on pre-defined features over a data set. The main goal of pattern recognition is to find and characterize structured information (called patterns) from information sources that present data in a dispersed manner. Different strategies allow for the identification of trends with the recognition of a specific descriptor (attribute) [19]. Two recognition strategies have been defined: Supervised and non-supervised. In the former, recognition is based on the presence of structured patterns. With this, the prediction of patterns of interest is carried out. At this stage, training is necessary, in order to determine the membership of the pattern to a specific class. In contrast, the latter strategy can be used to predict behaviors and relationships between the data, in order to determine the membership function of a class [20].  
We focus on supervised strategies, where the construction of the predictive model has the objective of assigning patterns to classes that have already been defined [21]. In general, using these approaches requires pre-established knowledge to train the model. Such methods base their computational strategy on the elimination of redundant variables and on the fast recognition of an entity with active patterns or first-order rules, with which it must comply to be recognized [22]. Each characteristic of the entity represents a variable that needs to be analyzed during the recognition process. So, through any machine learning algorithm, the entity is categorized. In recent years, research has provided machine learning mechanisms that can analyze a variable set and whose computational cost is acceptable for a data set that, in basic processing, can have an NP-order cost [23].  
One of these recognition models is Ar2p [8], which is based on the systematic functioning of the brain. This paper proposes its adaptation to develop a recognition model for WS selection in composition activities under cloud environments. Ar2p was originally developed from the pattern recognition theory of mind (PRTM), where the memory manages a hierarchy of patterns, which are perceived through our senses (conceptually called \"sensors\") [24]. As soon as one of our senses is activated, certain patterns can be recognized.  
A general model of Ar2p is shown in Figure 1, where each level represents a recognition module. Thus, a system can have several levels which offer the possibility of initially recognizing atomic patterns (properties with a single attribute/value); however, a pattern may pass through several levels until it is recognized, resulting in a complex pattern that underlies the processing of the sub-levels. Every recognition module's target is to recognize its corresponding pattern (i.e., a copy of the pattern), such that recognition can occur at any level. Ar2p defines two strategies to recognize a pattern: One by key signals and another by partial signal recognition. The former uses the importance weights of the input signals identified as key signals, while the latter uses the partial or total presence of the signals in a simplified model.  
A pattern can be recognized by a single key signal or by the total of the partial signals if and only if they pass through the observation space based on their recognition levels. The advantage of using Ar2p is its level of uniformity, as it ensures that each recognition function is the same. This process is executed recursively. Considering PRTM [25], the recognition process occurs when the brain can recognize a pattern through any sense. Thus, the brain's ability sharpens when developing a hierarchy of complex patterns (i.e., a learning process occurs).  
Below, we formalize the concept of composition and discuss how a composition is modeled to be used as input to an Ar_WSDS recognition process.