ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs

The outcome-oriented predictive process monitoring is a family of predictive process mining techniques that have witnessed rapid development and increasing adoption in the past few years. Boosted by the recent successful applications of deep learning in predictive process mining, we propose ORANGE, a novel deep learning method for learning outcome-oriented predictive process models. The main innovation of this study is that we adopt an imagery representation of the ongoing traces, which delineates potential data patterns that arise at neighbour pixels. Leveraging a collection of images representing ongoing traces, we train a Convolutional Neural Network (CNN) to predict the outcome of an ongoing trace. The empirical study shows the feasibility of the proposed method by investigating its accuracy on different benchmark outcome prediction problems in comparison to state-of-art competitor methods. In addition, we show how ORANGE can be integrated as an Intelligent Assistant into a CVM realized by MTM Project srl company to support sales agents in their negotiations. This case study shows that ORANGE can be effectively used to smartly monitor the outcome of ongoing negotiations by early highlighting negotiations that are candidate to be completed successfully

JARVIS: Joining Adversarial Training With Vision Transformers in Next-Activity Prediction

In this paper, we propose a novel predictive process monitoring approach, named JARVIS, that is designed to achieve a balance between accuracy and explainability in the task of next-activity prediction. To this aim, JARVIS represents different process executions (traces) as patches of an image and uses this patch-based representation within a multi-view learning scheme combined with Vision Transformers (ViTs). Using multi-view learning we guarantee good accuracy by leveraging the variety of information recorded in event logs as different patches of an image. The use of ViTs enables the integration of explainable elements directly into the framework of a predictive process model trained to forecast the next trace activity from the completed events in a running trace by utilizing self-attention modules that give paired attention values between two picture patches. Attention modules disclose explainable information concerning views of the business process and events of the trace that influenced the prediction. In addition, we explore the effect of ViT adversarial training to mitigate overfitting and improve the accuracy and robustness of predictive process monitoring. Experiments with various benchmark event logs prove the accuracy of JARVIS compared to several current state-of-the-art methods and draw insights from explanations recovered through the attention modules

Predictive Process Mining Meets Computer Vision

Nowadays predictive process mining is playing a fundamental role in the business scenario as it is emerging as an effective means to monitor the execution of any business running process. In particular, knowing in advance the next activity of a running process instance may foster an optimal management of resources and promptly trigger remedial operations to be carried out. The problem of next activity prediction has been already tackled in the literature by formulating several machine learning and process mining approaches. In particular, the successful milestones achieved in computer vision by deep artificial neural networks have recently inspired the application of such architectures in several fields. The original contribution of this work consists of paving the way for relating computer vision to process mining via deep neural networks. To this aim, the paper pioneers the use of an RGB encoding of process instances useful to train a 2-D Convolutional Neural Network based on Inception block. The empirical study proves the effectiveness of the proposed approach for next-activity prediction on different real-world event logs

Using convolutional neural networks for predictive process analytics

Predictive process monitoring has recently become one of the main enablers of data-driven insights in process mining. As an application of predictive analytics, process prediction is mainly concerned with predicting the evolution of running traces based on models extracted from historical event logs. This paper presents a process mining approach, which uses convolutional neural networks to equip the execution scenario of a business process with a means to predict the next activity in a running trace. The basic idea is to convert the temporal data enclosed in the historical event log of a business process into spatial data so as to treat them as images. To this purpose, every trace of the event log is first transformed into the set of its prefix traces (i.e. sequences of events that represent the prefix of a trace). These prefix traces are mapped into 2D image-like data structures. Created spatial data are finally used to train a Convolutional Neural Network, in order to learn a deep learning model capable to predict the next activity (i.e. the activity associated to the event occurring after the last event in the considered prefix trace). This predictive deep model can be employed as a powerful service to support participants in performing business processes since it guarantees a higher utilization by acting proactively in anticipation. Preliminary tests with two benchmark logs are carried out to investigate the viability of the proposed approach

FISDeT: Fuzzy Inference System Development Tool

This paper introduces FISDeT, a tool to support the design of Fuzzy Inference Systems, composed of a set of Python modules sharing the standard specification language FCL used for FIS definition. FISDeT includes a graphical user interface that enables easy definition and quick update of elements composing the knowledge base of a FIS. Given the knowledge base, the tool can perform the inference of fuzzy rules, providing the output of a FIS for any given input. Modules for creating a fuzzy rule base for classification and verifying the behavior of the classification system are integrated within the environment. The paper also reports applications of FISDeT to acquire available knowledge bases and create a FIS for an image processing task

A multi-view deep learning approach for predictive business process monitoring

The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces based on the knowledge learned from historical event logs. In this paper, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety of information possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease

A mobile app for contactless measurement of vital signs through remote Photoplethysmography

The healthcare domain has undergone a huge transformation thanks to the availability of new technologies. In particular, health monitoring systems have entered everyday life without interfering with the daily routine. Mobile phones are increasingly used as health monitoring systems by means of ad-hoc applications. In this work, we propose a mobile app for contactless monitoring of vital signs, such as heart rate and blood oxygen saturation. Differently from the other devices in the literature, it is able to measure vital signs from the analysis of short videos through the use of remote photoplethysmography technology. A client-server architecture has been developed to run the signal and video processing on the server while implementing video acquisition and communication with the user on the smartphone. Experiments have shown the effectiveness of the developed app in accurately measuring vital parameters