AI-Enhanced Hybrid Decision Management

The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach

Modeling, Executing and Monitoring IoT-Driven Business Rules in BPMN and DMN: Current Support and Challenges

The involvement of the Internet of Things (IoT) in Business Process Management (BPM) solutions is continuously increasing. While BPM enables the modeling, implementation, execution, monitoring, and analysis of business processes, IoT fosters the collection and exchange of data over the Internet. By enriching BPM solutions with real-world IoT data both process automation and process monitoring can be improved. Furthermore, IoT data can be utilized during process execution to realize IoT-driven business rules that consider the state of the physical environment. The aggregation of low-level IoT data into processrelevant, high-level IoT data is a paramount step towards IoT-driven business processes and business rules respectively. In this context, Business Process Modeling and Notation (BPMN) and Decision Model and Notation (DMN) provide support to model, execute, and monitor IoTdriven business rules, but some challenges remain. This paper derives the challenges that emerge when modeling, executing, and monitoring IoT-driven business rules using BPMN 2.0 and DMN standards

Process Model Metrics for Quality Assessment of Computer-Interpretable Guidelines in PROform

Background: Clinical Practice Guidelines (CPGs) include recommendations to optimize patient care and thus have the potential to improve the quality and outcomes of healthcare. To achieve this, CPG recommendations are usually formalized in terms of Computer-Interpretable Guideline (CIG) languages. However, a clear understanding of CIG models may prove complicated, due to the inherent complexity of CPGs and the specificities of CIG languages. Drawing a parallel with the Business Process Management (BPM) and the Software Engineering fields, understandability and modifiability of CIG models can be regarded as primary quality attributes, in order to facilitate their validation, as well as their adaptation to accommodate evolving clinical evidence, by modelers (typically teams made up of clinical and IT experts). This constitutes a novel approach in this area of CIG development, where understandability and modifiability aspects have not been considered to date. Objective: In this paper, we define a comprehensive set of process model metrics for CIGs described in the PROforma CIG language, with the main objective of providing tools for quality assessment of CIG models in this language. Methods: To this end, we first reinterpret a set of metrics from the BPM field in terms of PROforma and then we define new metrics to capture the singularities of PROforma models. Additionally, we report on a set of experiments to assess the relationship between the structural and logical properties of CIG models, as measured by the proposed metrics, and their understandability and modifiability from the point of view of modelers, both clinicians and IT staff. For the analysis of the experiment results, we perform statistical analysis based on a generalized linear mixed model with binary logistic regression. Results: Our contribution includes the definition of a comprehensive set of metrics that allow measuring model quality aspects of PROforma CIG models, the implementation of tools and algorithms to assess the metrics for PROforma models, and the empirical validation of the proposed metrics as quality indicators. Conclusions: In light of the results, we conclude that the proposed metrics can be of great value, as they capture the PROforma-specific features in addition to those inspired by the general-purpose BPM metrics in the literature. In particular, the newly defined metrics for PROforma prevail as statistically significant when the whole CIG model is considered, which means that they better characterize its complexity. Consequently, the proposed metrics can be used as quality indicators of the understandability, and thereby maintainability, of PROforma CIGs

What we know and what we do not know about DMN

The recent Decision Model and Notation (DMN) establishes business decisions as first-class citizens of executable business processes. This research note has two objectives: first, to describe DMN's technical and theoretical foundations; second, to identify research directions for investigating DMN's potential benefits on a technological, individual and organizational level. To this end, we integrate perspectives from management science, cognitive theory and information systems research

Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels

This paper provides estimation and inference methods for a large number of heterogeneous treatment effects in a panel data setting with many potential controls. We assume that heterogeneous treatment is the result of a low-dimensional base treatment interacting with many heterogeneity-relevant controls, but only a small number of these interactions have a non-zero heterogeneous effect relative to the average. The method has two stages. First, we use modern machine learning techniques to estimate the expectation functions of the outcome and base treatment given controls and take the residuals of each variable. Second, we estimate the treatment effect by l1-regularized regression (i.e., Lasso) of the outcome residuals on the base treatment residuals interacted with the controls. We debias this estimator to conduct pointwise inference about a single coefficient of treatment effect vector and simultaneous inference about the whole vector. To account for the unobserved unit effects inherent in panel data, we use an extension of correlated random effects approach of Mundlak (1978) and Chamberlain (1982) to a high-dimensional setting. As an empirical application, we estimate a large number of heterogeneous demand elasticities based on a novel dataset from a major European food distributor

On the enhancement of Big Data Pipelines through Data Preparation, Data Quality, and the distribution of Optimisation Problems

Nowadays, data are fundamental for companies, providing operational support by facilitating daily transactions. Data has also become the cornerstone of strategic decision-making processes in businesses. For this purpose, there are numerous techniques that allow to extract knowledge and value from data. For example, optimisation algorithms excel at supporting decision-making processes to improve the use of resources, time and costs in the organisation. In the current industrial context, organisations usually rely on business processes to orchestrate their daily activities while collecting large amounts of information from heterogeneous sources. Therefore, the support of Big Data technologies (which are based on distributed environments) is required given the volume, variety and speed of data. Then, in order to extract value from the data, a set of techniques or activities is applied in an orderly way and at different stages. This set of techniques or activities, which facilitate the acquisition, preparation, and analysis of data, is known in the literature as Big Data pipelines. In this thesis, the improvement of three stages of the Big Data pipelines is tackled: Data Preparation, Data Quality assessment, and Data Analysis. These improvements can be addressed from an individual perspective, by focussing on each stage, or from a more complex and global perspective, implying the coordination of these stages to create data workflows. The first stage to improve is the Data Preparation by supporting the preparation of data with complex structures (i.e., data with various levels of nested structures, such as arrays). Shortcomings have been found in the literature and current technologies for transforming complex data in a simple way. Therefore, this thesis aims to improve the Data Preparation stage through Domain-Specific Languages (DSLs). Specifically, two DSLs are proposed for different use cases. While one of them is a general-purpose Data Transformation language, the other is a DSL aimed at extracting event logs in a standard format for process mining algorithms. The second area for improvement is related to the assessment of Data Quality. Depending on the type of Data Analysis algorithm, poor-quality data can seriously skew the results. A clear example are optimisation algorithms. If the data are not sufficiently accurate and complete, the search space can be severely affected. Therefore, this thesis formulates a methodology for modelling Data Quality rules adjusted to the context of use, as well as a tool that facilitates the automation of their assessment. This allows to discard the data that do not meet the quality criteria defined by the organisation. In addition, the proposal includes a framework that helps to select actions to improve the usability of the data. The third and last proposal involves the Data Analysis stage. In this case, this thesis faces the challenge of supporting the use of optimisation problems in Big Data pipelines. There is a lack of methodological solutions that allow computing exhaustive optimisation problems in distributed environments (i.e., those optimisation problems that guarantee the finding of an optimal solution by exploring the whole search space). The resolution of this type of problem in the Big Data context is computationally complex, and can be NP-complete. This is caused by two different factors. On the one hand, the search space can increase significantly as the amount of data to be processed by the optimisation algorithms increases. This challenge is addressed through a technique to generate and group problems with distributed data. On the other hand, processing optimisation problems with complex models and large search spaces in distributed environments is not trivial. Therefore, a proposal is presented for a particular case in this type of scenario. As a result, this thesis develops methodologies that have been published in scientific journals and conferences.The methodologies have been implemented in software tools that are integrated with the Apache Spark data processing engine. The solutions have been validated through tests and use cases with real datasets