Towards data visualisation based on conceptual modelling

Selecting data, transformations and visual encodings in current data visualisation tools is undertaken at a relatively low level of abstraction - namely, on tables of data - and ignores the conceptual model of the data. Domain experts, who are likely to be familiar with the conceptual model of their data, may find it hard to understand tabular data representations, and hence hard to select appropriate data transformations and visualisations to meet their exploration or question-answering needs. We propose an approach that addresses these problems by defining a set of visualisation schema patterns that each characterise a group of commonly-used data visualisations, and by using knowledge of the conceptual schema of the underlying data source to create mappings between it and the visualisation schema patterns. To our knowledge, this is the first work to propose a conceptual modelling approach to matching data and visualisations

Generic unified modelling process for developing semantically rich, dynamic and temporal models

Models play a vital role in supporting a range of activities in numerous domains. We rely on models to support the design, visualisation, analysis and representation of parts of the world around us, and as such significant research effort has been invested into numerous areas of modelling; including support for model semantics, dynamic states and behaviour, temporal data storage and visualisation. Whilst these efforts have increased our capabilities and allowed us to create increasingly powerful software-based models, the process of developing models, supporting tools and /or data structures remains difficult, expensive and error-prone. In this paper we define from literature the key factors in assessing a model’s quality and usefulness: semantic richness, support for dynamic states and object behaviour, temporal data storage and visualisation. We also identify a number of shortcomings in both existing modelling standards and model development processes and propose a unified generic process to guide users through the development of semantically rich, dynamic and temporal models

Requirements for Topology in 3D GIS

Topology and its various benefits are well understood within the context of 2D Geographical Information Systems. However, requirements in three-dimensional (3D) applications have yet to be defined, with factors such as lack of users' familiarity with the potential of such systems impeding this process. In this paper, we identify and review a number of requirements for topology in 3D applications. The review utilises existing topological frameworks and data models as a starting point. Three key areas were studied for the purposes of requirements identification, namely existing 2D topological systems, requirements for visualisation in 3D and requirements for 3D analysis supported by topology. This was followed by analysis of application areas such as earth sciences and urban modelling which are traditionally associated with GIS, as well as others including medical, biological and chemical science. Requirements for topological functionality in 3D were then grouped and categorised. The paper concludes by suggesting that these requirements can be used as a basis for the implementation of topology in 3D. It is the aim of this review to serve as a focus for further discussion and identification of additional applications that would benefit from 3D topology. © 2006 The Authors. Journal compilation © 2006 Blackwell Publishing Ltd

Towards business integration as a service 2.0 (BIaaS 2.0)

Cloud Computing Business Framework (CCBF) is a framework for designing and implementation of Could Computing solutions. This proposal focuses on how CCBF can help to address linkage in Cloud Computing implementations. This leads to the development of Business Integration as a Service 1.0 (BIaaS 1.0) allowing different services, roles and functionalities to work together in a linkage-oriented framework where the outcome of one service can be input to another, without the need to translate between domains or languages. BIaaS 2.0 aims to allow automation, enhanced security, advanced risk modelling and improved collaboration between processes in BIaaS 1.0. The benefits from adopting BIaaS 1.0 and developing BIaaS 2.0 are illustrated using a case study from the University of Southampton and several collaborators including IBM US. BIaaS 2.0 can work with mainstream technologies such as scientific workflows, and the proposal and demonstration of BIaaS 2.0 will be aimed to certainly benefit industry and academia. © 2011 IEEE

Towards Business Integration as a Service 2.0

Cloud Computing Business Framework (CCBF) is a framework for designing and implementation of Could Computing solutions. This proposal focuses on how CCBF can help to address linkage in Cloud Computing implementations. This leads to the development of Business Integration as a Service 1.0 (BIaS 1.0) allowing different services, roles and functionalities to work together in a linkage-oriented framework where the outcome of one service can be input to another, without the need to translate between domains or languages. BIaS 2.0 aims to allow full automation, enhanced security, advanced risk modelling and improved collaboration between processes in BIaaS 1.0. The benefits from adopting BIaS 1.0 and developing BIaS 2.0 are illustrated using a case study from the University of Southampton and several collaborators including IBM US. BIaS 2.0 can work with mainstream technologies such as scientific workflows, and the proposal and demonstration of BIaaS 2.0 will certainly benefit industry and academia

Design Creativity: Future Directions for Integrated Visualisation

The Architecture, Engineering and Construction (AEC) sectors are facing unprecedented challenges, not just with increased complexity of projects per se, but design-related integration. This requires stakeholders to radically re-think their existing business models (and thinking that underpins them), but also the technological challenges and skills required to deliver these projects. Whilst opponents will no doubt cite that this is nothing new as the sector as a whole has always had to respond to change; the counter to this is that design ‘creativity’ is now much more dependent on integration from day one. Given this, collaborative processes embedded in Building Information Modelling (BIM) models have been proffered as a panacea solution to embrace this change and deliver streamlined integration. The veracity of design teams’ “project data” is increasingly becoming paramount - not only for the coordination of design, processes, engineering services, fabrication, construction, and maintenance; but more importantly, facilitate ‘true’ project integration and interchange – the actualisation of which will require firm consensus and commitment. This Special Issue envisions some of these issues, challenges and opportunities (from a future landscape perspective), by highlighting a raft of concomitant factors, which include: technological challenges, design visualisation and integration, future digital tools, new and anticipated operating environments, and training requirements needed to deliver these aspirations. A fundamental part of this Special Issue’s ‘call’ was to capture best practice in order to demonstrate how design, visualisation and delivery processes (and technologies) affect the finished product viz: design outcome, design procedures, production methodologies and construction implementation. In this respect, the use of virtual environments are now particularly effective at supporting the design and delivery processes. In summary therefore, this Special Issue presents nine papers from leading scholars, industry and contemporaries. These papers provide an eclectic (but cognate) representation of AEC design visualisation and integration; which not only uncovers new insight and understanding of these challenges and solutions, but also provides new theoretical and practice signposts for future research

A qualitative approach to the identification, visualisation and interpretation of repetitive motion patterns in groups of moving point objects

Discovering repetitive patterns is important in a wide range of research areas, such as bioinformatics and human movement analysis. This study puts forward a new methodology to identify, visualise and interpret repetitive motion patterns in groups of Moving Point Objects (MPOs). The methodology consists of three steps. First, motion patterns are qualitatively described using the Qualitative Trajectory Calculus (QTC). Second, a similarity analysis is conducted to compare motion patterns and identify repetitive patterns. Third, repetitive motion patterns are represented and interpreted in a continuous triangular model. As an illustration of the usefulness of combining these hitherto separated methods, a specific movement case is examined: Samba dance, a rhythmical dance will? many repetitive movements. The results show that the presented methodology is able to successfully identify, visualize and interpret the contained repetitive motions

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Sustainable urban development in practice:the SAVE concept

The need for sustainable development of the urban environment presents the research community with a number of challenges and opportunities. A considerable volume of research has been undertaken into the constituent parts of this complex problem and a number of tool kits and methodologies have been developed to enable and encourage the application of specific aspects of research in practice. However, there is limited evidence of the holistic integration of the body of knowledge arising from the research within real-life decision-making practices. In this paper we present an overview of the existing body of knowledge relating to sustainable development of the urban environment and propose a generic framework for its integration within current practices. This framework recognises the need to: understand social, economic, and environmental issues; understand the decision-making processes; provide a means of measurement, assessment, or valuation of the issues; provide analytical methods for the comparative assessment of complex data to enable an evaluation of strategies and design options and to communicate effectively throughout the process with a wide range of stakeholders. The components of a novel sustainability assessment, visualisation and enhancement (SAVE) framework, developed by the authors to ‘operationalise’ the body of knowledge are presented and justified. These include: decision-mapping methods to identify points of intervention; indicator identification and measurement approaches; appropriate mathematical and analytical tools and an interactive simulation and visualisation platform which integrates and communicates complex multivariate information to diverse stakeholder groups. We report on the application of the SAVE framework to a major urban development project and reflect on its current and potential impact on the development. Conclusions are also drawn about its general applicability

A Spatial Decision Support System for Property Valuation

May 6-