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By James A. Barnes

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Section 5 discusses related work. Section 6 concludes the paper and discusses the future work. 2 Fundamental Overview In this section, we present the preliminary about running logs, business process and the neighborhood context that are captured from them. We firstly present some definitions related to business process and illustrate a motivating example (Sect. 1). Then, we present why and how to make use of running logs (Sect. 2), and the manner to recommend activity based-on neighborhood context (Sect.

As a discriminative model, our approach can overcome that limitation and fully exploit the relationship underlying. 38 F. Yu et al. 6 Conclusion and Discussion We propose to analysis process and execution logs to provide modeling suggestions to developers depending on their last modeling action. As discriminative model, instead of generative models, average perceptron is able to combine workflow structure and logs, and makes full use of these abundant information with practical value as extracted feature.

In each step, the business designer selects an activity from our recommendation list (or choose another activity beyond our list) and creates links between it and previous activities. Fig. 2. An example when the Fig. 1 business process is creating ongoing 3 Recommendation Approach We propose to use a discriminative model named average perceptron [3] to train and predict the recommended activity. The reason to apply average perceptron is: (1) As linear architectures, it perform better than non-linear ones and other algorithm especially in high-dimensional sparse discrete feature space.

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