Specification and implementation of mapping rule visualization and editing : MapVOWL and the RMLEditor

Visual tools are implemented to help users in defining how to generate Linked Data from raw data. This is possible thanks to mapping languages which enable detaching mapping rules from the implementation that executes them. However, no thorough research has been conducted so far on how to visualize such mapping rules, especially if they become large and require considering multiple heterogeneous raw data sources and transformed data values. In the past, we proposed the RMLEditor, a visual graph-based user interface, which allows users to easily create mapping rules for generating Linked Data from raw data. In this paper, we build on top of our existing work: we (i) specify a visual notation for graph visualizations used to represent mapping rules, (ii) introduce an approach for manipulating rules when large visualizations emerge, and (iii) propose an approach to uniformly visualize data fraction of raw data sources combined with an interactive interface for uniform data fraction transformations. We perform two additional comparative user studies. The first one compares the use of the visual notation to present mapping rules to the use of a mapping language directly, which reveals that the visual notation is preferred. The second one compares the use of the graph-based RMLEditor for creating mapping rules to the form-based RMLx Visual Editor, which reveals that graph-based visualizations are preferred to create mapping rules through the use of our proposed visual notation and uniform representation of heterogeneous data sources and data values. (C) 2018 Elsevier B.V. All rights reserved

Integration of all FSSIM components within SEAMLESS-IF and a stand alone Graphical User Interface for FSSIM

Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,

A platform for discovering and sharing confidential ballistic crime data.

Criminal investigations generate large volumes of complex data that detectives have to analyse and understand. This data tends to be "siloed" within individual jurisdictions and re-using it in other investigations can be difficult. Investigations into trans-national crimes are hampered by the problem of discovering relevant data held by agencies in other countries and of sharing those data. Gun-crimes are one major type of incident that showcases this: guns are easily moved across borders and used in multiple crimes but finding that a weapon was used elsewhere in Europe is difficult. In this paper we report on the Odyssey Project, an EU-funded initiative to mine, manipulate and share data about weapons and crimes. The project demonstrates the automatic combining of data from disparate repositories for cross-correlation and automated analysis. The data arrive from different cultural/domains with multiple reference models using real-time data feeds and historical databases

Tissue enrichment analysis for C. elegans genomics

Background: Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these datasets and forming hypotheses based on them remains challenging. One way to analyze these datasets is to associate ontologies (hierarchical, descriptive vocabularies with controlled relations between terms) with genes and to look for enrichment of specific terms. Although Gene Ontology (GO) is available for Caenorhabditis elegans, it does not include anatomical information. Results: We have developed a tool for identifying enrichment of C. elegans tissues among gene sets and generated a website GUI where users can access this tool. Since a common drawback to ontology enrichment analyses is its verbosity, we developed a very simple filtering algorithm to reduce the ontology size by an order of magnitude. We adjusted these filters and validated our tool using a set of 30 gold standards from Expression Cluster data in WormBase. We show our tool can even discriminate between embryonic and larval tissues and can even identify tissues down to the single-cell level. We used our tool to identify multiple neuronal tissues that are down-regulated due to pathogen infection in C. elegans. Conclusions: Our Tissue Enrichment Analysis (TEA) can be found within WormBase, and can be downloaded using Python’s standard pip installer. It tests a slimmed-down C. elegans tissue ontology for enrichment of specific terms and provides users with a text and graphic representation of the results

Process control and configuration of a reconfigurable production system using a multi-agent software system

Thesis (M. Tech. (Information Technology)) -- Central University of technology, Free State, 2011Traditional designs for component-handling platforms are rigidly linked to the product being produced. Control and monitoring methods for these platforms consist of various proprietary hardware controllers containing the control logic for the production process. Should the configuration of the component handling platform change, the controllers need to be taken offline and reprogrammed to take the changes into account. The current thinking in component-handling system design is the notion of re-configurability. Reconfigurability means that with minimum or no downtime the system can be adapted to produce another product type or overcome a device failure. The re-configurable component handling platform is built-up from groups of independent devices. These groups or cells are each responsible for some aspect of the overall production process. By moving or swopping different versions of these cells within the component-handling platform, re-configurability is achieved. Such a dynamic system requires a flexible communications platform and high-level software control architecture to accommodate the reconfigurable nature of the system. This work represents the design and testing of the core of a re-configurable production control software platform. Multiple software components work together to control and monitor a re-configurable component handling platform. The design and implementation of a production database, production ontology, communications architecture and the core multi-agent control application linking all these components together is presented

Artificial intelligence applied to software testing:a tertiary study

Context: Artificial intelligence (AI) methods and models have extensively been applied to support different phases of the software development lifecycle, including software testing (ST). Several secondary studies investigated the interplay between AI and ST but restricted the scope of the research to specific domains or sub-domains within either area.Objective: This research aims to explore the overall contribution of AI to ST, while identifying the most popular applications and potential paths for future research directions.Method: We executed a tertiary study following well-established guidelines for conducting systematic literature mappings in software engineering and for answering nine research questions.Results: We identified and analyzed 20 relevant secondary studies. The analysis was performed by drawing from well-recognized AI and ST taxonomies and mapping the selected studies according to them. The resulting mapping and discussions provide extensive and detailed information on the interplay between AI and ST.Conclusion: The application of AI to support ST is a well-consolidated and growing interest research topic. The mapping resulting from our study can be used by researchers to identify opportunities for future research, and by practitioners looking for evidence-based information on which AI-supported technology to possibly adopt in their testing processes