Predictive Process Monitoring Methods: Which One Suits Me Best?

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for companies to navigate in this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying existing work on predictive process monitoring. This objective is achieved by systematically identifying, categorizing, and analyzing existing approaches for predictive process monitoring. The review is then used to develop a value-driven framework that can support organizations to navigate in the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques

LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep Learning (IEEE DL'17) @ SSC

Predictive Business Process Monitoring with tree-based classification algorithms

Predictive business process monitoring is a current research area which purpose is to predict the outcome of a whole process (or an element of a process i.e. a single event or task) based on available data. In the article we explore the possibility of use of the machine learning classification algorithms based on trees (CART, C5.0, random forest and extreme gradient boosting) in order to anticipate the result of a process. We test the application of these algorithms on real world event-log data and compare it with the known approaches. Our results show tha

Comparative analysis of clustering-based remaining-time predictive process monitoring approaches

Predictive process monitoring aims to accurately predict a variable of interest (e.g. remaining time) or the future state of the process instance (e.g. outcome or next step). Various studies have been explored to develop models with greater predictive power. However, comparing the various studies is difficult as different datasets, parameters and evaluation measures have been used. This paper seeks to address this problem with a focus on studies that adopt a clustering-based approach to predict the remaining time to the end of the process instance. A systematic literature review is undertaken to identify existing studies that adopt a clustering-based remaining-time predictive process monitoring approach and performs a comparative analysis to compare and benchmark the output of the identified studies using five real-life event logs

PREDIKSI STATUS PENGIRIMAN BARANG MENGGUNAKAN METODE MACHINE LEARNING

One of the key performance indicators for the logistics industry, especially freight forwarder company (cargo), is the delivery time. This is still a challenge in this industry in terms of ensuring the customer service level and reducing transportation costs. On the other hand, the development of information technology now allows an organization or company to collect large amounts of data automatically. A decent method that can be used to analyze the data for prediction purposes is machine learning, which is a method of extracting data into a certain pattern of information. This research aims to apply three machine learning methods to estimate the status of shipping goods. The method used in this study follows the machine learning process published by the Cross Industry Standard Process for Data Mining (CRISP-DM), namely; business processes understanding, data understanding, data preparation, model development, evaluation, and implementation. Based on the results of the study, the random forest method produces better accuracy than the logistic regression and artificial neural network (ANN) methods, which is 76.6%, while the results of ANN and logistic regression methods are 73.81% and 72.84% respectively.  Salah satu ukuran kinerja bagi industri logistik, khususnya perusahaan pengiriman barang, adalah ketepatan waktu penghantaran. Hal ini masih menjadi tantangan bagi perusahaan guna menjamin tingkat kepuasan pelanggan dan menurunkan biaya transportasi. Di sisi lain, perkembangan teknologi informasi saat ini memungkinkan organisasi atau perusahaan dapat mengumpulkan data dalam jumlah besar secara otomatis. Metode yang cukup andal yang dapat digunakan dalam melakukan analisis data prediksi adalah machine learning, yaitu metode ekstraksi data menjadi sebuah pola informasi tertentu. Penelitian ini bertujuan untuk mengaplikasikan tiga metode machine learning untuk memperkirakan status pengiriman barang. Metodologi yang digunakan pada penelitian ini mengikuti proses machine learning yang dirilis oleh the Cross Industry Standard Process for Data Mining (CRISP-DM), yaitu; memahami proses bisnis, memahami data, persiapan data, pengembangan model, evaluasi, dan implementasi. Berdasarkan hasil penelitian, metode random forest menghasilkan nilai akurasi yang lebih baik jika dibandingkan dengan metode regresi logistik dan artificial neural network (ANN), yaitu sebesar 76,6%, sedangkan metode ANN dan regresi logistik sebesar 73,81% dan 72,84%. Kata kunci: analisis data prediksi, machine learning, waktu pengiriman, transportasi dan logisti

Specification-Driven Predictive Business Process Monitoring

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.Comment: This article significantly extends the previous work in https://doi.org/10.1007/978-3-319-91704-7_7 which has a technical report in arXiv:1804.00617. This article and the previous work have a coauthor in commo