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This CEUR-WS volume contains the joint proceedings of two workshops on the domain of computational intelligence: the first International Workshop on Computational Intelligence for Process Mining (CI4PM 2022) and the first International Workshop on Pervasive Artificial Intelligence (PAI 2022). Both events were co-located with the fortieth IEEE International Joint Conference on Neural Networks (IJCNN 2022), organized within the twelfth IEEE World Congress on Computational Intelligence (WCCI 2022).The University of Padua (Università degli Studi di Padova) served as the hosting institution for WCCI 2022, which took place between the 18ᵗʰ and the 23ʳᵈ of July 2022 in Padua, Italy. The accepted papers of CI4PM were presented on the 18ᵗʰ of July, while accepted papers of PAI were presented on the 19ᵗʰ of July. Additional information on the individual events, accepted papers, and the respective committees can be found on the following pages.

During the last decade several decision mining techniques have been developed to discover the decision perspective of a process from an event log. The increasing number of decision mining techniques raises the importance of evaluating the quality of the discovered decision models and/or decision logic. Currently, the evaluations are limited because of the small amount of available event logs with decision information. To alleviate this limitation, this paper introduces the ‘DataExtend’ technique that allows evaluating and comparing decision-mining techniques with each other, using a sufficient number of event logs and process models to generate evaluation results that are statistically significant. This paper also reports on an initial evaluation using ‘DataExtend’ that involves two techniques to discover decisions, whose results illustrate that the approach can serve the purpose.

Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof. Existing techniques in this field are able to predict, at each step of a case, the likelihood that the case will end up in an undesired outcome. These techniques, however, do not take into account what process workers may do with the generated predictions in order to decrease the likelihood of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects. The framework incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptive process monitoring in a given setting. The paper also outlines an approach to optimize the generation of alarms given a dataset and a set of cost model parameters. The proposed approach is empirically evaluated using a range of real-life event logs.

Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to reduce the risk of failure. This paper discusses monitoring, predicting, and recommending using a PAR system within a financial institute in the Netherlands to avoid faulty executions. While predictions were based on the analysis of historical data, the most opportune intervention was selected on the basis of human judgment and subjective opinions. The results showed that, while the predictions of risky cases were relatively accurate, no reduction was observed in the number of faulty executions. We believe that this was caused by incorrect choices of interventions. While a large body of research exists on monitoring and predicting based on facts recorded in historicaldata, research on fact-based interventions is relatively limited. This paper reports on lessons learned from the case study in finance and proposes a new methodology to improve the performances of PAR systems. This methodology advocates the importance of several cycles of interactions among all actors involved so as to develop interventions that incorporate their feedback and are based on insights from factual, historical data., Technical report

Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to reduce the risk of failure. This paper discusses monitoring, predicting, and recommending using a PAR system within a financial institute in the Netherlands to avoid faulty executions. Although predictions were based on the analysis of historical data, the most opportune intervention was selected on the basis of human judgment and subjective opinions. The results showed that, although the predictions of risky cases were relatively accurate, no reduction was observed in the number of faulty executions. We believe that this was caused by incorrect choices of interventions. Although a large body of research exists on monitoring and predicting based on facts recorded in historical data, research on fact-based interventions is relatively limited. This paper reports on lessons learned from the case study in finance and identifies the need to develop interventions based on insights from factual, historical data.

Process Mining is a relatively young research discipline that aims to discover, monitor and improve processes based on real facts (and not assumptions) by extracting knowledge from event logs readily available in today’s (information) systems [1]. The lion’s share of attention of Process Mining has been devoted to Process Discovery,namely extracting process models - mainly business process models - from an event log. In the last decade, several new techniques for process discovery have been put forward. Each technique has been evaluated on separate event data, thus making it difficult to perform a comparative evaluation. However, in light of a continuously growing of strength and interest in Process Mining as a discipline, it becomes crucial to finally foster a comparison of existing discovery techniques. With this need at hand, we organized the first edition of the Process-Discovery contest, which was co-located with the BPM-2016 Conference in Rio de Janeiro (Brazil).

Process mining offers techniques to exploit event data by providing insights and recommendations to improve business processes. The growing amount of algorithms for process discovery has raised the question of which algorithms perform best on a given event log. Current evaluation frameworks for empirically evaluating discovery techniques depend on the notation used (behavioral identical models may give different results) and cannot provide more general statements about populations of models. Therefore, this paper proposes a new integrated evaluation framework that uses a classification approach to make it modeling notation independent. Furthermore, it is founded on experimental design to ensure the generalization of results. It supports two main evaluation objectives: benchmarking process discovery algorithms and sensitivity analysis, i.e. studying the effect of model and log characteristics on a discovery algorithm's accuracy. The framework is designed as a scientific workflow which enables automated, extendable and shareable evaluation experiments. An extensive experiment including four discovery algorithms and six control-flow characteristics validates the relevance and flexibility of the framework. Ultimately, the paper aims to advance the state-of-the-art for evaluating process discovery techniques.

Process mining methods analyze an organization’s processes by using process execution data. During the handling of a process instance data about the execution of activities is recorded. Process mining uses such data to gain insights about the real execution of processes. In this thesis, we address research challenges in which a multi-perspective view on processes is needed and that look beyond the control-flow perspective, which defines the sequence of activities of a process. We consider problems in which multiple interacting process perspectives — in particular control-flow, data, resources, time, and functions — are considered together. The contributed methods span several types of process mining: two are concerned with conformance checking, two are process discovery techniques, and one is a decision mining method. All methods have been implemented, evaluated, and applied in the context of four case studies.

During the last decade several decision mining techniques have been developed to discover the decision perspective of a process from an event log. The increasing number of decision mining techniques raises the importance of evaluating the quality of the discovered decision models and/or decision logic. Currently, the evaluations are limited because of the small amount of available event logs with decision information. To alleviate this limitation, this paper introduces the ‘DataExtend’ technique that allows to evaluate and compare decision-mining techniques with each other, using a sufficient number of event logs and process models to generate evaluation results that are statistically significant. This paper also reports on an initial evaluation using ‘DataExtend’ that involves two techniques to discover decisions, whose results illustrate that the approach can serve the purpose.