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

DMN for Data Quality Measurement and Assessment

Data Quality assessment is aimed at evaluating the suitability of a dataset for an intended task. The extensive literature on data quality describes the various methodologies for assessing data quality by means of data profiling techniques of the whole datasets. Our investigations are aimed to provide solutions to the need of automatically assessing the level of quality of the records of a dataset, where data profiling tools do not provide an adequate level of information. As most of the times, it is easier to describe when a record has quality enough than calculating a qualitative indicator, we propose a semi-automatically business rule-guided data quality assessment methodology for every record. This involves first listing the business rules that describe the data (data requirements), then those describing how to produce measures (business rules for data quality measurements), and finally, those defining how to assess the level of data quality of a data set (business rules for data quality assessment). The main contribution of this paper is the adoption of the OMG standard DMN (Decision Model and Notation) to support the data quality requirement description and their automatic assessment by using the existing DMN engines.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33Ministerio de Ciencia y Tecnología RTI2018-094283-B-C31European Regional Development Fund SBPLY/17/180501/00029

Applying the Decision Model and Notation in Practice: A Method to Design and Specify Business Decisions and Business Logic

Proper decision-making is one of the most important capabilities of an organization. Therefore, it is important to make explicit all decisions that are relevant to manage for an organization. In 2015 the Object Management Group published the Decision Model and Notation (DMN) standard that focuses on modelling business decisions and underlying business logic. DMN is being adopted at an increas-ing rate, however, theory does not adequately cover activities or methods to guide practitioners mod-elling with DMN. To tackle this problem this paper presents a method to guide the modelling process of business decisions with DMN. The method has been validated and improved with an experiment using thirty participants. Based on this method, future research could focus on further validation and improvement by using more participants from different industries

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

A morphospace of functional configuration to assess configural breadth based on brain functional networks

The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. In this study, we use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information: 11 pages, 5 figure

Effects of Quantitative Measures on Understanding Inconsistencies in Business Rules

Business Rules have matured to an important aspect in the development of organizations, encoding company knowledge as declarative constraints, aimed to ensure compliant business. The management of business rules is widely acknowledged as a challenging task. A problem here is a potential inconsistency ofbusiness rules, as business rules are often created collaboratively. To support companies in managing inconsistency, many works have suggested that a quantification of inconsistencies could provide valuable insights. However, the actual effects of quantitative insights in business rules management have not yet been evaluated. In this work, we present the results of an empirical experiment using eye-tracking and other performance measures to analyze the effects of quantitative measures on understanding inconsistencies in business rules. Our results indicate that quantitative measures are associated with better understanding accuracy, understanding efficiency and less mental effort in business rules management