A new demo modelling tool that facilitates model transformations

The age of digitization requires rapid design and re-design of enterprises. Rapid changes can be realized using conceptual modelling. The design and engineering methodology for organizations (DEMO) is an established modelling method for representing the organization domain of an enterprise. However, heterogeneity in enterprise design stakeholders generally demand for transformations between conceptual modelling languages. Specifically, in the case of DEMO, a transformation into business process modelling and notation (BPMN) models is desirable to account to both, the semantic sound foundation of the DEMO models, and the wide adoption of the de-facto industry standard BPMN. Model transformation can only be efficiently applied if tool support is available. Our research starts with a state-of-the-art analysis, comparing existing DEMO modelling tools. Using a design science research approach, our main contribution is the development of a DEMO modelling tool on the ADOxx platform. One of the main features of our tool is that it addresses stakeholder heterogeneity by enabling transformation of a DEMO organization construction diagram (OCD) into a BPMN collaboration diagram. A demonstration case shows the feasibility of our newly developed tool.http://www.springer.com/series/7911hj2021Industrial and Systems Engineerin

Empirical evaluation of a new DEMO modelling tool that facilitates model transformations

The engineering methodology for organizations (DEMO) incorporates an organization construction diagram (OCD) and transaction product table (TPT) to depict a consolidated representation of the enterprise in terms of actor roles that coordinate in consistent patterns on different transaction kinds. Although managers find the OCD useful due to its high level of abstraction, enterprise implementers and operators prefer detailed flow-chart-like models to guide their operations, such as business process model and notation (BPMN) models. BPMN models are prevalent in industry and offer modeling flexibility, but the models are often incomplete, since they are not derived from theoreticallybased, consistent coordination patterns. This study addresses the need to develop a DEMO modeling tool that incorporates the novel feature of transforming userselected parts of a validated OCD, consistently and in a semi-automated way, into BPMN collaboration diagrams. The contribution of this article is two-fold: (1) to demonstrate the utility of the new DEMO-ADOxx modelling tool, including its model transformation ability; and (2) to empirically evaluate the usability of the tool.Paper presented at the ER 2020 Workshop, 39th International Conference on Conceptual Modeling, November 3-6, 2020 in Vienna, Austria.https://er2020.big.tuwien.ac.at/workshopsIndustrial and Systems Engineerin

The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysi

Third Conference on Artificial Intelligence for Space Applications, part 2

Topics relative to the application of artificial intelligence to space operations are discussed. New technologies for space station automation, design data capture, computer vision, neural nets, automatic programming, and real time applications are discussed

Visual and interactive exploration of point data

Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine scales of resolution. For instance, socio-economic attributes are commonly assigned to UPC. Hence, they can be represented as points and observable at the postcode level. Using UPC as a common field allows the concatenation of variables from disparate data sources that can potentially support sophisticated spatial analysis. However, visualising UPC in urban areas has at least three limitations. First, at small scales UPC occurrences can be very dense making their visualisation as points difficult. On the other hand, patterns in the associated attribute values are often hardly recognisable at large scales. Secondly, UPC can be used as a common field to allow the concatenation of highly multivariate data sets with an associated postcode. Finally, socio-economic variables assigned to UPC (such as the ones used here) can be non-Normal in their distributions as a result of a large presence of zero values and high variances which constrain their analysis using traditional statistics. This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system developed to visually explore point data. Various well-known visualisation techniques were implemented to enable their interactive and dynamic interrogation. PVT provides multiple representations of point data to facilitate the understanding of the relations between attributes or variables as well as their spatial characteristics. Brushing between alternative views is used to link several representations of a single attribute, as well as to simultaneously explore more than one variable. PVT’s functionality shows how the use of visual techniques embedded in an interactive environment enable the exploration of large amounts of multivariate point data