Äriprotsesside ajaliste näitajate selgitatav ennustav jälgimine

Kaasaegsed ettevõtte infosüsteemid võimaldavad ettevõtetel koguda detailset informatsiooni äriprotsesside täitmiste kohta. Eelnev koos masinõppe meetoditega võimaldab kasutada andmejuhitavaid ja ennustatavaid lähenemisi äriprotsesside jõudluse jälgimiseks. Kasutades ennustuslike äriprotsesside jälgimise tehnikaid on võimalik jõudluse probleeme ennustada ning soovimatu tegurite mõju ennetavalt leevendada. Tüüpilised küsimused, millega tegeleb ennustuslik protsesside jälgimine on “millal antud äriprotsess lõppeb?” või “mis on kõige tõenäolisem järgmine sündmus antud äriprotsessi jaoks?”. Suurim osa olemasolevatest lahendustest eelistavad täpsust selgitatavusele. Praktikas, selgitatavus on ennustatavate tehnikate tähtis tunnus. Ennustused, kas protsessi täitmine ebaõnnestub või selle täitmisel võivad tekkida raskused, pole piisavad. On oluline kasutajatele seletada, kuidas on selline ennustuse tulemus saavutatud ning mida saab teha soovimatu tulemuse ennetamiseks. Töö pakub välja kaks meetodit ennustatavate mudelite konstrueerimiseks, mis võimaldavad jälgida äriprotsesse ning keskenduvad selgitatavusel. Seda saavutatakse ennustuse lahtivõtmisega elementaarosadeks. Näiteks, kui ennustatakse, et äriprotsessi lõpuni on jäänud aega 20 tundi, siis saame anda seletust, et see aeg on moodustatud kõikide seni käsitlemata tegevuste lõpetamiseks vajalikust ajast. Töös võrreldakse omavahel eelmainitud meetodeid, käsitledes äriprotsesse erinevatest valdkondadest. Hindamine toob esile erinevusi selgitatava ja täpsusele põhinevale lähenemiste vahel. Töö teaduslik panus on ennustuslikuks protsesside jälgimiseks vabavaralise tööriista arendamine. Süsteemi nimeks on Nirdizati ning see süsteem võimaldab treenida ennustuslike masinõppe mudeleid, kasutades nii töös kirjeldatud meetodeid kui ka kolmanda osapoole meetodeid. Hiljem saab treenitud mudeleid kasutada hetkel käivate äriprotsesside tulemuste ennustamiseks, mis saab aidata kasutajaid reaalajas.Modern enterprise systems collect detailed data about the execution of the business processes they support. The widespread availability of such data in companies, coupled with advances in machine learning, have led to the emergence of data-driven and predictive approaches to monitor the performance of business processes. By using such predictive process monitoring approaches, potential performance issues can be anticipated and proactively mitigated. Various approaches have been proposed to address typical predictive process monitoring questions, such as what is the most likely continuation of an ongoing process instance, or when it will finish. However, most existing approaches prioritize accuracy over explainability. Yet in practice, explainability is a critical property of predictive methods. It is not enough to accurately predict that a running process instance will end up in an undesired outcome. It is also important for users to understand why this prediction is made and what can be done to prevent this undesired outcome. This thesis proposes two methods to build predictive models to monitor business processes in an explainable manner. This is achieved by decomposing a prediction into its elementary components. For example, to explain that the remaining execution time of a process execution is predicted to be 20 hours, we decompose this prediction into the predicted execution time of each activity that has not yet been executed. We evaluate the proposed methods against each other and various state-of-the-art baselines using a range of business processes from multiple domains. The evaluation reaffirms a fundamental trade-off between explainability and accuracy of predictions. The research contributions of the thesis have been consolidated into an open-source tool for predictive business process monitoring, namely Nirdizati. It can be used to train predictive models using the methods described in this thesis, as well as third-party methods. These models are then used to make predictions for ongoing process instances; thus, the tool can also support users at runtime

Scalable Privacy-Compliant Virality Prediction on Twitter

The digital town hall of Twitter becomes a preferred medium of communication for individuals and organizations across the globe. Some of them reach audiences of millions, while others struggle to get noticed. Given the impact of social media, the question remains more relevant than ever: how to model the dynamics of attention in Twitter. Researchers around the world turn to machine learning to predict the most influential tweets and authors, navigating the volume, velocity, and variety of social big data, with many compromises. In this paper, we revisit content popularity prediction on Twitter. We argue that strict alignment of data acquisition, storage and analysis algorithms is necessary to avoid the common trade-offs between scalability, accuracy and privacy compliance. We propose a new framework for the rapid acquisition of large-scale datasets, high accuracy supervisory signal and multilanguage sentiment prediction while respecting every privacy request applicable. We then apply a novel gradient boosting framework to achieve state-of-the-art results in virality ranking, already before including tweet's visual or propagation features. Our Gradient Boosted Regression Tree is the first to offer explainable, strong ranking performance on benchmark datasets. Since the analysis focused on features available early, the model is immediately applicable to incoming tweets in 18 languages.Comment: AffCon@AAAI-19 Best Paper Award; Presented at AAAI-19 W1: Affective Content Analysi

Explainable Predictive and Prescriptive Process Analytics of customizable business KPIs

Recent years have witnessed a growing adoption of machine learning techniques for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring. Predictive analytics leverages machine learning and data analytics techniques to predict the future outcome of a process based on historical data. Therefore, the goal of predictive analytics is to identify future trends, and discover potential issues and anomalies in the process before they occur, allowing organizations to take proactive measures to prevent them from happening, optimizing the overall performance of the process. Prescriptive analytics systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running process in order to improve the outcome of a process, which can be defined in various ways depending on the business goals; this can involve measuring process-specific Key Performance Indicators (KPIs), such as costs, execution times, or customer satisfaction, and using this data to make informed decisions about how to optimize the process. This Ph.D. thesis research work has focused on predictive and prescriptive analytics, with particular emphasis on providing predictions and recommendations that are explainable and comprehensible to process actors. In fact, while the priority remains on giving accurate predictions and recommendations, the process actors need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way and they need to be convinced that the recommended actions are the most suitable ones to maximize the KPI of interest; otherwise, users would not trust and follow the provided predictions and recommendations, and the predictive technology would not be adopted.Recent years have witnessed a growing adoption of machine learning techniques for business improvement across various fields. Among other emerging applications, organizations are exploiting opportunities to improve the performance of their business processes by using predictive models for runtime monitoring. Predictive analytics leverages machine learning and data analytics techniques to predict the future outcome of a process based on historical data. Therefore, the goal of predictive analytics is to identify future trends, and discover potential issues and anomalies in the process before they occur, allowing organizations to take proactive measures to prevent them from happening, optimizing the overall performance of the process. Prescriptive analytics systems go beyond purely predictive ones, by not only generating predictions but also advising the user if and how to intervene in a running process in order to improve the outcome of a process, which can be defined in various ways depending on the business goals; this can involve measuring process-specific Key Performance Indicators (KPIs), such as costs, execution times, or customer satisfaction, and using this data to make informed decisions about how to optimize the process. This Ph.D. thesis research work has focused on predictive and prescriptive analytics, with particular emphasis on providing predictions and recommendations that are explainable and comprehensible to process actors. In fact, while the priority remains on giving accurate predictions and recommendations, the process actors need to be provided with an explanation of the reasons why a given process execution is predicted to behave in a certain way and they need to be convinced that the recommended actions are the most suitable ones to maximize the KPI of interest; otherwise, users would not trust and follow the provided predictions and recommendations, and the predictive technology would not be adopted

The Explainable Business Process (XBP) - An Exploratory Research

Providing explanations to the business process, its decisions and its activities, is an important key factor for the process in order to achieve the business objectives of the business process, and to minimize and deal with the ambiguity of the business process that causes multiple interpretations, as well as to engender the appropriate trust of the users in the process. As a first step towards adding explanations to business process, we present an exploratory study to bring in the concept of explainability into business process, where we propose a conceptual framework to use the explainability with business process in a model that we called the Explainable Business Process XBP, furthermore we propose the XBP lifecycle based on the Model-based and Incremental Knowledge Engineering (MIKE) approach, in order to show in details the phase where explainability can take a place in business process lifecycle, noting that we focus on explaining the decisions and activities of the process in its as-is model without transforming it into a to-be model

Contextual and Ethical Issues with Predictive Process Monitoring

This thesis addresses contextual and ethical issues in the predictive process monitoring framework and several related issues. Regarding contextual issues, even though the importance of case, process, social and external contextual factors in the predictive business process monitoring framework has been acknowledged, few studies have incorporated these into the framework or measured their impact. Regarding ethical issues, we examine how human agents make decisions with the assistance of process monitoring tools and provide recommendation to facilitate the design of tools which enables a user to recognise the presence of algorithmic discrimination in the predictions provided. First, a systematic literature review is undertaken to identify existing studies which adopt a clustering-based remaining-time predictive process monitoring approach, and a comparative analysis is performed to compare and benchmark the output of the identified studies using 5 real-life event logs. This curates the studies which have adopted this important family of predictive process monitoring approaches but also facilitates comparison as the various studies utilised different datasets, parameters, and evaluation measures. Subsequently, the next two chapter investigate the impact of social and spatial contextual factors in the predictive process monitoring framework. Social factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. The impact of social contextual features in the predictive process monitoring framework is investigated utilising a survival analysis approach. The proposed approach is benchmarked against existing approaches using five real-life event logs and outperforms these approaches. Spatial context (a type of external context) is also shown to improve the predictive power of business process monitoring models. The penultimate chapter examines the nature of the relationship between workload (a process contextual factor) and stress (a social contextual factor) by utilising a simulation-based approach to investigate the diffusion of workload-induced stress in the workplace. In conclusion, the thesis examines how users utilise predictive process monitoring (and AI) tools to make decisions. Whilst these tools have delivered real benefits in terms of improved service quality and reduction in processing time, among others, they have also raised issues which have real-world ethical implications such as recommending different credit outcomes for individuals who have an identical financial profile but different characteristics (e.g., gender, race). This chapter amalgamates the literature in the fields of ethical decision making and explainable AI and proposes, but does not attempt to validate empirically, propositions and belief statements based on the synthesis of the existing literature, observation, logic, and empirical analogy

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

Performance-preserving event log sampling for predictive monitoring

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy

Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making