The Internet of Things (IoT) is a networking paradigm where interconnected, smart objects continuously generate data and transmit it over the Internet. Much of the IoT initiatives are geared towards manufacturing low-cost and energy-efficient hardware for these objects, as well as the communication technologies that provide objects interconnectivity. However, the solutions to manage and utilize the massive volume of data produced by these objects are yet to mature. Traditional database management solutions fall short in satisfying the sophisticated application needs of an IoT network that has a truly global-scale. Current solutions for IoT data management address partial aspects of the IoT environment with special focus on sensor networks. In this paper, we survey the data management solutions that are proposed for IoT or subsystems of the IoT. We highlight the distinctive design primitives that we believe should be addressed in an IoT data management solution, and discuss how they are approached by the proposed solutions. We finally propose a data management framework for IoT that takes into consideration the discussed design elements and acts as a seed to a comprehensive IoT data management solution. The framework we propose adapts a federated, data- and sources-centric approach to link the diverse Things with their abundance of data to the potential applications and services that are envisioned for IoT.

Predictive Process Monitoring (PPM) has been integrated into process mining use cases as a value-adding task. PPM provides useful predictions on the future of the running business processes with respect to different perspectives, such as the upcoming activities to be executed next, the final execution outcome, and performance indicators. In the context of PPM, Machine Learning (ML) techniques are widely employed. In order to gain trust of stakeholders regarding the reliability of PPM predictions, eXplainable Artificial Intelligence (XAI) methods have been increasingly used to compensate for the lack of transparency of most of predictive models. Multiple XAI methods exist providing explanations for almost all types of ML models. However, for the same data, as well as, under the same preprocessing settings or same ML models, generated explanations often vary significantly. Corresponding variations might jeopardize the consistency and robustness of the explanations and, subsequently, the utility of the corresponding model and pipeline settings. This paper introduces a framework that enables the analysis of the impact PPM-related settings and ML-model-related choices may have on the characteristics and expressiveness of the generated explanations. Our framework provides a means to examine explanations generated either for the whole reasoning process of an ML model, or for the predictions made on the future of a certain business process instance. Using well-defined experiments with different settings, we uncover how choices made through a PPM workflow affect and can be reflected through explanations. This framework further provides the means to compare how different characteristics of explainability methods can shape the resulting explanations and reflect on the underlying model reasoning process.

As a use case of process mining, Predictive Process Monitoring (PPM) aims to provide information on the future course of running business process instances. A large number of available PPM approaches adopt predictive models based on Machine Learning (ML). With the improved efficiency and accuracy of ML models usually being coupled with increasing complexity, their understandability becomes compromised. Having the user at the center of attention, various eXplainable Artificial Intelligence (XAI) methods emerged to provide users with explanations of the reasoning process of an ML model. Though there is a growing interest in applying XAI methods to PPM results, various proposals have been made to evaluate explanations according to different criteria. In this article, we propose an approach to quantitatively evaluate XAI methods concerning their ability to reflect the facts learned from the underlying stores of business-related data, i.e., event logs. Our approach includes procedures to extract features that are crucial for generating predictions. Moreover, it computes ratios that have proven to be useful in differentiating XAI methods. We conduct experiments that produce useful insights into the effects of the various choices made through a PPM workflow. We can show that underlying data and model issues can be highlighted using the applied XAI methods. Furthermore, we could penalize and reward XAI methods for achieving certain levels of consistency with the facts learned about the underlying data. Our approach has been applied to different real-life event logs using different configurations of the PPM workflow.

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

Predictive process monitoring (PPM) has been discussed as a use case of process mining for several years. PPM enables foreseeing the future of an ongoing business process by predicting, for example, relevant information on the way in which running processes terminate or on related process performance indicators. A large share of PPM approaches adopt Machine Learning (ML), taking advantage of the accuracy and precision of ML models. Consequently, PPM inherits the challenges of traditional ML approaches. One of these challenges concerns the need to gain user trust in the generated predictions. This issue is addressed by explainable artificial intelligence (XAI). However, in addition to ML characteristics, the choices made and the techniques applied in the context of PPM influence the resulting explanations. This necessitates the availability of a study on the effects of different choices made in the context of a PPM task on the explainability of the generated predictions. In order to address this gap, we systemically investigate the effects of different PPM settings on the data fed into an ML model and subsequently into the employed XAI method. We study how differences between the resulting explanations indicate several issues in the underlying data. Example of these issues include collinearity and high dimensionality of the input data. We construct a framework for performing a series of experiments to examine different choices of PPM dimensions (i.e., event logs, preprocessing configurations, and ML models), integrating XAI as a fundamental component. In addition to agreements, the experiments highlight several inconsistencies between data characteristics and important predictors used by the ML model on one hand, and explanations of predictions of the investigated ML model on the other.

Although predictions based on machine learning are reaching unprecedented levels of accuracy, understanding the underlying mechanisms of a machine learning model is far from trivial. Therefore, explaining machine learning outcomes is gaining more interest with an increasing need to understand, trust, justify, and improve both the predictions and the prediction process. This, in turn, necessitates providing mechanisms to evaluate explainability methods as well as to measure their ability to fulfill their designated tasks. In this paper, we introduce a technique to extract the most important features from a data perspective. We propose metrics to quantify the ability of an explainability method to convey and communicate the underlying concepts available in the data. Furthermore, we evaluate the ability of an eXplainable Artificial Intelligence (XAI) method to reason about the reliance of a Machine Learning (ML) model on the extracted features. Through experiments, we further, prove that our approach enables differentiating explainability methods independent of the underlying experimental settings. The proposed metrics can be used to functionally evaluate the extent to which an explainability method is able to extract the patterns discovered by a machine learning model. Our approach provides a means to quantitatively differentiate global explainability methods in order to deepen user trust not only in the predictions generated but also in their explanations.