## 5 Evaluation  
### Workload Analysis  
We use the real-life open-source dataset of German credit fraud data provided by Hamburg University for experimentation [17]. The dataset is chosen due to the reason that no specialized knowledge is required to understand the addressed application. Several changes were planted into the German Credit data to be found by the system and be verified if it can be found accordingly. The dataset contains two classes, as \"good\" and \"bad\" credits, and about 20 attributes of each and every person with a status of an existing checking account, credit history, amount, purpose, employment information, guarantors, properties, number of credit cards, and so on. For workload analysis purposes, we use three different classifiers, i.e., a naive Bayes classifier, multilayer perceptron (MLP), and sequential minimal optimization (SMO).  
The naive Bayes classifier is a probabilistic classifier based on applying [PERSON] theorem [18] with strong or naive independence assumptions between features. Numeric estimator precision values are chosen based on an analysis of the training data. Due to this reason, the classifier is not an updatable classifier and typically initiated with zero training instances. The updatable version is a naive Bayes updatable classifier which uses the default precision of 0.1 for numeric attributes when called with zero training instances [19]. It is highly scalable, requiring a number of parameters linear in the number of features in a learning problem. Maximum-likelihood training, in this case, can be done through evaluating a closed-form expression, which takes linear time, rather than by complex and expensive iterative approximation as used for many other types of classifiers.  
MLP is a class of feedforward artificial neural network, where each MLP consist of at least three layer nodes: an input layer, a hidden layer, and an output layer. Each node is a neuron, except for the input nodes and each neuron uses a nonlinear activation function. MLP utilizes backpropagation supervised learning technique for training [20]. It can distinguish non-linearly separable data due to its multiple layers and non-linear activation. Its network can be built by hand, created by an algorithm, or both. The network can also be modified and monitored during training time.  
SMO is usually used for solving the quadratic programming problems that arise during the support vector machines training [21]. It can globally replace all the missing values and transforms nominal attributes into binary ones. It also normalizes all the attributes by default. In order to obtain proper probability estimates, an option that fits calibration models to the outputs of the support vector should be selected. The predicted probabilities are coupled using the pairwise coupling method [22].  
We analyze the German credit fraud dataset using the above algorithms. The results are shown in Figure 12. The columns indicate correctly classified instances, incorrectly classified instances, Kappa statistic, mean absolute error, root mean squared error, relative absolute error, and root relative squared error accordingly. Naive Bayes classifies the highest correct number of instances and with the lowest root relative error and root mean squared error. SMO has the lowest relative absolute error and means absolute error accordingly. The model building time is summarized in Table 1, showing that Naive Bayes algorithm has the ability to model the scenario in the lowest time of the other two algorithms despite the number of tuples in the dataset. Naive Bayes has a 5.2 s model building time, SMO has 10.3 s, and multilayer perceptron has a 21.19 s model building time. The resultant analysis of the workload is transferred to the Job Manager at runtime to be used by the decision process module for the system later on. We are currently working on the custom adaptor to make it happened and will be published with our upcoming research work.