# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
## 2 Related Work  
Predictive Business Process Monitoring (PBPM) is a category of process mining that aims to predict a so-called Measure of Interest (MOI) from a process that is still running (incomplete) by utilizing past event logs data consisting of completed processes [2]. Based on the type of output, we can divide PBPM research into several types. The first is research that focuses on predicting all things relating to the time of a process, be it delay, remaining time, or deadline violation. The second is research that focuses on predicting the output of a process, such as whether the process is a normal process or an abnormal process, so that violation can be predicted and prevented as early as possible. The third is research that focuses on predicting succeeding events, such as what activity will occur in those events or what resources will be involved in them. In addition to the research that focuses on types of outcomes, there is also research that focuses on the problem of the optimization of the hyperparameters of the model used. In this subsection, we will discuss the related research as sorted by the time of publication. The first research relevant to time prediction, especially remaining-time prediction, is the work of [PERSON] et al. [6]. Their proposed method uses, as the baseline, non-parametric regression and only the remaining time average those processes have at a given time. The work by [PERSON] et al. [3] is the first to utilize process mining techniques to predict time-related MOI. The authors demonstrate that we can evolve the discovered process model by utilizing a transition system to predict the remaining time of ongoing processes. They also introduced several types of abstraction techniques that could be applied to balance situations of \"overfitting\" or \"underfitting.\" To make the model aware of the context in predicting processing time, [PERSON] et al. [7] took advantage of contextual features such as availability of resources and dependence on other attributes. [PERSON] et al. [2] extended the work of [PERSON] et al. [7] and formalized the problem of PBPM in general. [PERSON] et al. [8] proposed a method that considers the attribute data loaded by the event rather than just the sequence of activities (simple symbolic sequence) to predict the most probable outcome (positive or negative) of a running process. For their method's machine-learning model, they used Random Forest. The Queuing Theory proposed by [PERSON] et al. [9] predicts a delay in an ongoing process. As in [PERSON] et al. [3], the authors also used annotated transition systems in this prediction task. Besides the work of [PERSON] et al. [2], [PERSON] et al. [10] also proposed a general framework that correlatesprocesses in the event log for predictive purposes. However, the proposed method cannot handle the feature explosion problem that will arise due to the lack of a feature space reduction mechanism. In machine learning, there is the \"no free lunch theorem,\" whereby a particular model with a hyperparameter configuration is excellent in a specific dataset but awful in another. For that reason, the configuration of the hyperparameters needs to be optimized and adjusted to the specific problem and the dataset encountered. Therefore, [PERSON] et al. [11] proposed a framework that provides an automatic mechanism for optimizing the hyperparameter model used in the area of PBPM. A solution using deep learning was first proposed by [PERSON] et al. [12] Their deep-learning architecture consists of two hidden recurrent neural networks (RNN) layers using long short-term memory (LSTM) cells. [PERSON] et al. [13] also used the LSTM architecture. This model is more general because it can predict multiple objectives at once, such as time-related properties, case outcomes, and future event(s). The same model was proposed by [PERSON] et al. in the same year. Even though it focuses only on one type of prediction--that is, remaining-time prediction--it considers the data attributes attached to the event.