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

Prescriptive Business Process Monitoring for Recommending Next Best Actions

Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN`s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique`s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances

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

A European aerosol phenomenology - 7: High-time resolution chemical characteristics of submicron particulate matter across Europe

Similarities and differences in the submicron atmospheric aerosol chemical composition are analyzed from a unique set of measurements performed at 21 sites across Europe for at least one year. These sites are located between 35 and 62°N and 10° W – 26°E, and represent various types of settings (remote, coastal, rural, industrial, urban). Measurements were all carried out on-line with a 30-min time resolution using mass spectroscopy based instruments known as Aerosol Chemical Speciation Monitors (ACSM) and Aerosol Mass Spectrometers (AMS) and following common measurement guidelines. Data regarding organics, sulfate, nitrate and ammonium concentrations, as well as the sum of them called non-refractory submicron aerosol mass concentration ([NR-PM1]) are discussed. NR-PM1 concentrations generally increase from remote to urban sites. They are mostly larger in the mid-latitude band than in southern and northern Europe. On average, organics account for the major part (36–64%) of NR-PM1 followed by sulfate (12–44%) and nitrate (6–35%). The annual mean chemical composition of NR-PM1 at rural (or regional background) sites and urban background sites are very similar. Considering rural and regional background sites only, nitrate contribution is higher and sulfate contribution is lower in mid-latitude Europe compared to northern and southern Europe. Large seasonal variations in concentrations (μg/m³) of one or more components of NR-PM1 can be observed at all sites, as well as in the chemical composition of NR-PM1 (%) at most sites. Significant diel cycles in the contribution to [NR-PM1] of organics, sulfate, and nitrate can be observed at a majority of sites both in winter and summer. Early morning minima in organics in concomitance with maxima in nitrate are common features at regional and urban background sites. Daily variations are much smaller at a number of coastal and rural sites. Looking at NR-PM1 chemical composition as a function of NR-PM1 mass concentration reveals that although organics account for the major fraction of NR-PM1 at all concentration levels at most sites, nitrate contribution generally increases with NR-PM1 mass concentration and predominates when NR-PM1 mass concentrations exceed 40 μg/m³ at half of the sites. © 2021 The Author

A European aerosol phenomenology - 7 : High-time resolution chemical characteristics of submicron particulate matter across Europe

Similarities and differences in the submicron atmospheric aerosol chemical composition are analyzed from a unique set of measurements performed at 21 sites across Europe for at least one year. These sites are located between 35 and 62 degrees N and 10 degrees W - 26 degrees E, and represent various types of settings (remote, coastal, rural, industrial, urban). Measurements were all carried out on-line with a 30-min time resolution using mass spectroscopy based instruments known as Aerosol Chemical Speciation Monitors (ACSM) and Aerosol Mass Spectrometers (AMS) and following common measurement guidelines. Data regarding organics, sulfate, nitrate and ammonium concentrations, as well as the sum of them called non-refractory submicron aerosol mass concentration ([NR-PM1]) are discussed. NR-PM1 concentrations generally increase from remote to urban sites. They are mostly larger in the mid-latitude band than in southern and northern Europe. On average, organics account for the major part (36-64%) of NR-PM1 followed by sulfate (12-44%) and nitrate (6-35%). The annual mean chemical composition of NR-PM1 at rural (or regional background) sites and urban background sites are very similar. Considering rural and regional background sites only, nitrate contribution is higher and sulfate contribution is lower in midlatitude Europe compared to northern and southern Europe. Large seasonal variations in concentrations (mu g/m(3)) of one or more components of NR-PM1 can be observed at all sites, as well as in the chemical composition of NR-PM1 (%) at most sites. Significant diel cycles in the contribution to [NR-PM1] of organics, sulfate, and nitrate can be observed at a majority of sites both in winter and summer. Early morning minima in organics in concomitance with maxima in nitrate are common features at regional and urban background sites. Daily variations are much smaller at a number of coastal and rural sites. Looking at NR-PM1 chemical composition as a function of NR-PM1 mass concentration reveals that although organics account for the major fraction of NR-PM1 at all concentration levels at most sites, nitrate contribution generally increases with NR-PM1 mass concentration and predominates when NR-PM1 mass concentrations exceed 40 mu g/m(3) at half of the sites.Peer reviewe

A European aerosol phenomenology - 7: High-time resolution chemical characteristics of submicron particulate matter across Europe

Similarities and differences in the submicron atmospheric aerosol chemical composition are analyzed from a unique set of measurements performed at 21 sites across Europe for at least one year. These sites are located between 35 and 62°N and 10° W – 26°E, and represent various types of settings (remote, coastal, rural, industrial, urban). Measurements were all carried out on-line with a 30-min time resolution using mass spectroscopy based instruments known as Aerosol Chemical Speciation Monitors (ACSM) and Aerosol Mass Spectrometers (AMS) and following common measurement guidelines. Data regarding organics, sulfate, nitrate and ammonium concentrations, as well as the sum of them called non-refractory submicron aerosol mass concentration ([NR-PM1]) are discussed. NR-PM1 concentrations generally increase from remote to urban sites. They are mostly larger in the mid-latitude band than in southern and northern Europe. On average, organics account for the major part (36–64%) of NR-PM1 followed by sulfate (12–44%) and nitrate (6–35%). The annual mean chemical composition of NR-PM1 at rural (or regional background) sites and urban background sites are very similar. Considering rural and regional background sites only, nitrate contribution is higher and sulfate contribution is lower in mid-latitude Europe compared to northern and southern Europe. Large seasonal variations in concentrations (μg/m³) of one or more components of NR-PM1 can be observed at all sites, as well as in the chemical composition of NR-PM1 (%) at most sites. Significant diel cycles in the contribution to [NR-PM1] of organics, sulfate, and nitrate can be observed at a majority of sites both in winter and summer. Early morning minima in organics in concomitance with maxima in nitrate are common features at regional and urban background sites. Daily variations are much smaller at a number of coastal and rural sites. Looking at NR-PM1 chemical composition as a function of NR-PM1 mass concentration reveals that although organics account for the major fraction of NR-PM1 at all concentration levels at most sites, nitrate contribution generally increases with NR-PM1 mass concentration and predominates when NR-PM1 mass concentrations exceed 40 μg/m³ at half of the sites

Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances of a process, including predictions of the remaining cycle time of running cases of a process. A number of approaches to tackle this latter prediction problem have been proposed in the literature. However, due to differences in the experimental setups, choice of datasets, evaluation measures and baselines, the relative performance of various methods remains unclear. This article presents a systematic review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 methods based on 16 real-life datasets