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

Drag it together with Groupie: making RDF data authoring easy and fun for anyone

One of the foremost challenges towards realizing a “Read-write Web of Data” [3] is making it possible for everyday computer users to easily find, manipulate, create, and publish data back to the Web so that it can be made available for others to use. However, many aspects of Linked Data make authoring and manipulation difficult for “normal” (ie non-coder) end-users. First, data can be high-dimensional, having arbitrary many properties per “instance”, and interlinked to arbitrary many other instances in a many different ways. Second, collections of Linked Data tend to be vastly more heterogeneous than in typical structured databases, where instances are kept in uniform collections (e.g., database tables). Third, while highly flexible, the problem of having all structures reduced as a graph is verbosity: even simple structures can appear complex. Finally, many of the concepts involved in linked data authoring - for example, terms used to define ontologies are highly abstract and foreign to regular citizen-users.To counter this complexity we have devised a drag-and-drop direct manipulation interface that makes authoring Linked Data easy, fun, and accessible to a wide audience. Groupie allows users to author data simply by dragging blobs representing entities into other entities to compose relationships, establishing one relational link at a time. Since the underlying representation is RDF, Groupie facilitates the inclusion of references to entities and properties defined elsewhere on the Web through integration with popular Linked Data indexing services. Finally, to make it easy for new users to build upon others’ work, Groupie provides a communal space where all data sets created by users can be shared, cloned and modified, allowing individual users to help each other model complex domains thereby leveraging collective intelligence

Teegi: Tangible EEG Interface

We introduce Teegi, a Tangible ElectroEncephaloGraphy (EEG) Interface that enables novice users to get to know more about something as complex as brain signals, in an easy, en- gaging and informative way. To this end, we have designed a new system based on a unique combination of spatial aug- mented reality, tangible interaction and real-time neurotech- nologies. With Teegi, a user can visualize and analyze his or her own brain activity in real-time, on a tangible character that can be easily manipulated, and with which it is possible to interact. An exploration study has shown that interacting with Teegi seems to be easy, motivating, reliable and infor- mative. Overall, this suggests that Teegi is a promising and relevant training and mediation tool for the general public.Comment: to appear in UIST-ACM User Interface Software and Technology Symposium, Oct 2014, Honolulu, United State

Development of a pilot data management infrastructure for biomedical researchers at University of Manchester – approach, findings, challenges and outlook of the MaDAM Project

Management and curation of digital data has been becoming ever more important in a higher education and research environment characterised by large and complex data, demand for more interdisciplinary and collaborative work, extended funder requirements and use of e-infrastructures to facilitate new research methods and paradigms. This paper presents the approach, technical infrastructure, findings, challenges and outlook (including future development within the successor project, MiSS) of the ‘MaDAM: Pilot data management infrastructure for biomedical researchers at University of Manchester’ project funded under the infrastructure strand of the JISC Managing Research Data (JISCMRD) programme. MaDAM developed a pilot research data management solution at the University of Manchester based on biomedical researchers’ requirements, which includes technical and governance components with the flexibility to meet future needs across multiple research groups and disciplines

Developing LCA-based benchmarks for sustainable consumption - for and with users

This article presents the development process of a consumer-oriented, illustrative benchmarking tool enabling consumers to use the results of environmental life cycle assessment (LCA) to make informed decisions. Active and environmentally conscious consumers and environmental communicators were identified as key target groups for this type of information. A brochure presenting the benchmarking tool was developed as an participatory, iterative process involving consumer focus groups, stakeholder workshops and questionnaire-based feedback. In addition to learning what works and what does not, detailed suggestions on improved wording and figures were obtained, as well as a wealth of ideas for future applications

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

Configuring Hierarchical Layouts to Address Research Questions

We explore the effects of selecting alternative layouts in hierarchical displays that show multiple aspects of large multivariate datasets, including spatial and temporal characteristics. Hierarchical displays of this type condition a dataset by multiple discrete variable values, creating nested graphical summaries of the resulting subsets in which size, shape and colour can be used to show subset properties. These 'small multiples' are ordered by the conditioning variable values and are laid out hierarchically using dimensional stacking. Crucially, we consider the use of different layouts at different hierarchical levels, so that the coordinates of the plane can be used more effectively to draw attention to trends and anomalies in the data. We argue that these layouts should be informed by the type of conditioning variable and by the research question being explored. We focus on space-filling rectangular layouts that provide data-dense and rich overviews of data to address research questions posed in our exploratory analysis of spatial and temporal aspects of property sales in London. We develop a notation ('HiVE') that describes visualisation and layout states and provides reconfiguration operators, demonstrate its use for reconfiguring layouts to pursue research questions and provide guidelines for this process. We demonstrate how layouts can be related through animated transitions to reduce the cognitive load associated with their reconfiguration whilst supporting the exploratory process