Exploring Interpretability for Predictive Process Analytics

Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for example, when will the process instance complete and what will be the outcome upon completion. Machine learning models can be trained on event log data recording historical process execution to build the underlying predictive models. Multiple techniques have been proposed so far which encode the information available in an event log and construct input features required to train a predictive model. While accuracy has been a dominant criterion in the choice of various techniques, they are often applied as a black-box in building predictive models. In this paper, we derive explanations using interpretable machine learning techniques to compare and contrast the suitability of multiple predictive models of high accuracy. The explanations allow us to gain an understanding of the underlying reasons for a prediction and highlight scenarios where accuracy alone may not be sufficient in assessing the suitability of techniques used to encode event log data to features used by a predictive model. Findings from this study motivate the need and importance to incorporate interpretability in predictive process analytics.Comment: 15 pages, 7 figure

ЭКОЛОГИЧЕСКИЕ И ЭНЕРГОСБЕРЕГАЮЩИЕ АСПЕКТЫ ПРИ ПРОЕКТИРОВАНИИ СИСТЕМ ГИДРОПРИВОДОВ МАШИН

The paper gives a brief analysis in the field of creation of biologically split pressure fluids and lubricants. Characteristics and properties of biologically decomposed МГ-46БР oil which has been developed at the BNTU are presented in the paper. The paper contains a scheme of a laboratory-scale plant and a methodology for experimental comparative estimation of fluid property influence on power indices of a hydraulic drive. It has been shown that according to its properties and a power-saving criterion the МГ-46БР oil meets the standard requirements and it can be applied in the systems of power hydraulic drives of mobile and technological machinery as a working fluid which is an altemative to mineral oils.Дан краткий анализ в области создания биологически расщепляемых гидравлических жидкостей и смазок. Приведены характеристики и свойства разработанного в БНТУ биологически разлагаемого масла МГ-46БР, а также схема лабораторной установки и методика экспериментальной сравнительной оценки влияния свойств жидкости на энергетические показатели гидропривода. Показано, чго по своим свойствам и критерию энергосбережения масло МГ-46БР соответствует требованиям стандартов и может применяться в системах силовых гидроприводов мобильных и технологических машин в качестве рабочей жидкости, альтернативной минеральным маслам

XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs

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

Helpdesk

This dataset contains events from a ticketing management process of the help desk of an Italian software company. The process consists of 9 activities, and all cases start with the insertion of a new ticket into the ticketing management system. Each case ends when the issue is resolved and the ticket is closed. This log contains 3804 process instances (a.k.a "cases") and 13710 event

Hepdesk anonymized

This event log describes the ticketing management process of the help desk of a software compan

ECOLOGICAL AND POWER-SAVING ASPECTS WHILE DESIGNING MACHINERY HYDRAULIC DRIVE SYSTEMS

The paper gives a brief analysis in the field of creation of biologically split pressure fluids and lubricants. Characteristics and properties of biologically decomposed МГ-46БР oil which has been developed at the BNTU are presented in the paper. The paper contains a scheme of a laboratory-scale plant and a methodology for experimental comparative estimation of fluid property influence on power indices of a hydraulic drive. It has been shown that according to its properties and a power-saving criterion the МГ-46БР oil meets the standard requirements and it can be applied in the systems of power hydraulic drives of mobile and technological machinery as a working fluid which is an altemative to mineral oils