![]() ![]() Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. This program is a complete tool that lets you monitor absolutely all the active processes on your system, letting you set establish all kinds of filters to fine-tune any searches you may want to carry out. ![]() ![]() The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Process Monitor is a program that greatly expands the options available on the traditional Windows process monitor. ![]()
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