The DAGS Model: Relevance to Environmental Decision Support Systems

Environmental decision support systems (EDSS) involve both theoretical and applied concerns. Theoretical in terms of decision making and applied in terms of the development of actual systems to support decision making related to environmental issues. In this paper we describe a general research framework for conducting IS design research (the DAGS model), and show how that model is relevant to EDSS research. We argue that the dual goals of contributing to both theory and practice can at least in part be realized by more emphasis on the use of engineering and architecture as reference disciplines, and the use of Design science, Action research, Grounded theory, and Systems development as the components for this framework. The framework is illustrated with projects related to environmental issues

A closed-form approach to Bayesian inference in tree-structured graphical models

We consider the inference of the structure of an undirected graphical model in an exact Bayesian framework. More specifically we aim at achieving the inference with close-form posteriors, avoiding any sampling step. This task would be intractable without any restriction on the considered graphs, so we limit our exploration to mixtures of spanning trees. We consider the inference of the structure of an undirected graphical model in a Bayesian framework. To avoid convergence issues and highly demanding Monte Carlo sampling, we focus on exact inference. More specifically we aim at achieving the inference with close-form posteriors, avoiding any sampling step. To this aim, we restrict the set of considered graphs to mixtures of spanning trees. We investigate under which conditions on the priors - on both tree structures and parameters - exact Bayesian inference can be achieved. Under these conditions, we derive a fast an exact algorithm to compute the posterior probability for an edge to belong to {the tree model} using an algebraic result called the Matrix-Tree theorem. We show that the assumption we have made does not prevent our approach to perform well on synthetic and flow cytometry data

Multi-Methodological Approaches in Design Science: A Review, Proposal and Application

Multi-methodological research approaches have been strongly recommended for adoption to guide information systems (IS) research and deal with the complexities involved in the research. These research approaches require appropriate mapping and integration of multiple research methodologies. However, this is not an easy task to accomplish due to a series of philosophical, cultural and psychological issues involved. By reviewing and analyzing existing representative multi-methodological design science approaches, we identify that each approach has its own strengths and weaknesses. There is a clear gap between the need of multi-methodological approaches and the support from the existing research frameworks. To address these problems and issues, we propose an integrated multi-methodological research framework, which integrates strengths of the representative multi-methodological approaches and remedies their deficiencies. We demonstrate the application of the proposed framework by applying it to guide a research project in the field of information visualization, and discuss how the framework is deployed to address research problems/issues/ requirements and fulfill research objectives

A posteriori metadata from automated provenance tracking: Integration of AiiDA and TCOD

In order to make results of computational scientific research findable, accessible, interoperable and re-usable, it is necessary to decorate them with standardised metadata. However, there are a number of technical and practical challenges that make this process difficult to achieve in practice. Here the implementation of a protocol is presented to tag crystal structures with their computed properties, without the need of human intervention to curate the data. This protocol leverages the capabilities of AiiDA, an open-source platform to manage and automate scientific computational workflows, and TCOD, an open-access database storing computed materials properties using a well-defined and exhaustive ontology. Based on these, the complete procedure to deposit computed data in the TCOD database is automated. All relevant metadata are extracted from the full provenance information that AiiDA tracks and stores automatically while managing the calculations. Such a protocol also enables reproducibility of scientific data in the field of computational materials science. As a proof of concept, the AiiDA-TCOD interface is used to deposit 170 theoretical structures together with their computed properties and their full provenance graphs, consisting in over 4600 AiiDA nodes

Extracting tag hierarchies

Tagging items with descriptive annotations or keywords is a very natural way to compress and highlight information about the properties of the given entity. Over the years several methods have been proposed for extracting a hierarchy between the tags for systems with a "flat", egalitarian organization of the tags, which is very common when the tags correspond to free words given by numerous independent people. Here we present a complete framework for automated tag hierarchy extraction based on tag occurrence statistics. Along with proposing new algorithms, we are also introducing different quality measures enabling the detailed comparison of competing approaches from different aspects. Furthermore, we set up a synthetic, computer generated benchmark providing a versatile tool for testing, with a couple of tunable parameters capable of generating a wide range of test beds. Beside the computer generated input we also use real data in our studies, including a biological example with a pre-defined hierarchy between the tags. The encouraging similarity between the pre-defined and reconstructed hierarchy, as well as the seemingly meaningful hierarchies obtained for other real systems indicate that tag hierarchy extraction is a very promising direction for further research with a great potential for practical applications.Comment: 25 pages with 21 pages of supporting information, 25 figure

Studying collaboration and annotation as factors in achieving trust in electronic documents

This report presents two master student graduation studies on trust and collaboration when searching for information on the Internet. The studies were done as part of the SLIM project with Swedish Law and Informatics Research Institute, Faculty of Law, Stockholm University, financed by The Bank of Sweden Tercentenary Foundation (Stiftelsen Riksbankens Jubileumsfond)

Bayesian Dynamic DAG Learning: Application in Discovering Dynamic Effective Connectome of Brain

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization \textbf{(BDyMA)} method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover bidirected edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and baseline methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process