基于过程挖掘的未来感知预测模型

将事件日志中蕴含的过程模型看成两紧邻活动的组合，提出两种新的过程模型。首先，利用日志信息中的活动紧邻关系构造邻接矩阵提取过程模型，该模型中每个活动仅发生一次;其次，为避免过程模型中出现回路或者环路而造成模型预测精度降低的情况发生，在构造的邻接矩阵中增加活动在事件日志中所处的顺序位次，构造含有活动位次信息的邻接矩阵，以此为基础上进一步提取过程模型，该模型中每个活动在同一个位次上仅发生一次；再次，通过矩阵中的信息可获得过程模型中每个上层节点到各个下层节点的路径与相应概率；接下来，根据事件日志中信息的类型和特征，利用过程模型对决策者所需要的信息（如活动名称、等待时间、发生概率）进行预测；最后，利用随机数据与实际数据同基于序列提取规则的过程模型预测结果进行比较，验证所提模型的实际有效性。

Future aware prediction model based on process mining

Viewing the process model in event logs as the combination of the two adjacent activities, two novel process models are proposed. First, the process model is extracted by constructing adjacency matrix, taking advantage of the adjacency relationships of activities in the event logs. To improve the prediction accuracy of the model, loops are avoided in the process model. So, each activity in this model will only happen once. Second, the serial number of activities in the event logs to the adjacency matrix is added, constructing a new adjacency matrix with sequence information. Based on the new adjacency matrix, the process model is extracted. Each activity in this model will only happen once at the same sequence position. Third, with the adjacency matrix, the path from each prior node to next nodes in the process model and their corresponding probabilities are gotten. Then, according to the type and characteristic information of the event logs, predictions of the information are made which are needed by decision makers, e.g. activity name, waiting time, and probability based on process model. Finally, the effectiveness of the proposed models by comparing the prediction results of random data and real data based on process models is verified.