# SCoPE: Service Composition and Personalization Environment
## 5 ECE Environment
### Step: 2--Adherence Calculation  
Considering that _Adherence_ represents a compatibility relation between the individual customer and a solution \(sp_{ij}\) fragment, it is calculated by estimating three coefficients: _Closeness_ (_CL_); _Partial Adherence_ (_PA_); and _Adherence_ (_AD_). _CL_ is represented by a multidimensional matrix of the proximity between customer's requests and the features of the {service, provider} pair fragment. _PA_ is an intermediate computation that refines the closeness based on ponderation between customer's relevance (_RL_) and service's coverage level (_CO_) for each care need. Finally, _AD_ represents the resulting adherence considering all care needs together. Table 6 summarizes the adherence calculation process, including the purpose, inputs, and outputs for each sub-step. The three sub-steps should be repeated for each \(sp_{ij}\) fragment resulted from the _Scope Filtering_ step. Let's see these sub-steps in more details:  
_Sub-step 1--CL calculation._ Since we aim at service personalization and adaptability for each customer, particular consideration is put on comparing solutions with the customer's profile and requests. To find the solution that has the best adherence, the assessment it based on each customer's requirement. _CL_ considers the _distance_ between customer's requests and the related features of \(sp_{ij}\). The larger the distance is, the smaller _CL_ is. As each customer has different needs/requests, the same service and provider fragment can have a different closeness to each customer. Currently, _CL_ calculation considers the following situations: (a) when the customer's request and the features of {service, provider} fragment are quantitatively expressed (crisp); (b) when the customer's request and the features of the {service, provider} fragment are qualitatively expressed (fuzzy); and (c) when the customer's request is not previously planned in the ECE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Adherence Calculation Process Steps**} & \multicolumn{2}{c}{**Purpose**} & \multicolumn{2}{c}{**Input**} & \multicolumn{1}{c}{**Output**} \\ \hline \multirow{6}{*}{_Repeated for each_ \(sp_{ij}\) fragment_} & Sub-step: 1 & Calculate the closeness & 1. \(sp_{ij}\) fragments & \multirow{6}{*}{\begin{tabular}{c} Closeness vector \\ (_CL_) \\ \end{tabular} } \\  & Closeness & \begin{tabular}{c} Fragment against the \\ customer’s requirements. \\ \end{tabular} & \begin{tabular}{c} 2. customer’s \\ requirements \\ \end{tabular} & \begin{tabular}{c} Closeness vector \\ (_CL_) \\ \end{tabular} \\ \cline{2-5}  & \multirow{4}{*}{_Repeated for each_ \(sp_{ij}\) fragment_} & Calculate the _Partial_ & 1. _CL_ & \\  & & \
\begin{tabular}{c} _Alherence_ coefficient: \\ Combining the _CL_ and the \\ service coverage level \\ (_CO_) in relation to the \\ customer’s care need \\ relevance. \\ \end{tabular} & 1. _CL_ & \\  & & & & 2. Customer’s care \\ \cline{2-5}  & \multirow{4}{*}{_Repeated for each_ \(sp_{ij}\) fragment_} & Sub-step: 3 & Calculate the _Adherence_ & \multirow{4}{*}{
\begin{tabular}{c} 3. Service Coverage \\ Level regarding the \\ \end{tabular} } \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Adherence calculation process. Figure 6: Mr. [PERSON]’s care needs sub-taxonomy and available \(sp_{ij}\) fragments. The initial classification of the {service, provider} pair features can be given by the ECE stakeholders (e.g., service provider members, customers, or ECE manager), by professionals in elderly care, or derived from statistical data. Some values might not be available yet (e.g., the service is not rated yet because it is new in the ECE) or not necessary for the customer (e.g., the customer has no constraints). It is considered that all {service, provider} fragments that offer equal or better features in comparison with customer's requests have a \(cl_{i}\) corresponding to the optimal solution (\(cl_{i}=1\)).