A Descriptive Framework for Temporal Data Visualizations Based on Generalized Space-Time Cubes

International audienceWe present the generalized space-time cube, a descriptive model for visualizations of temporal data. Visualizations are described as operations on the cube, which transform the cube's 3D shape into readable 2D visualizations. Operations include extracting subparts of the cube, flattening it across space or time or transforming the cubes geometry and content. We introduce a taxonomy of elementary space-time cube operations and explain how these operations can be combined and parameterized. The generalized space-time cube has two properties: (1) it is purely conceptual without the need to be implemented, and (2) it applies to all datasets that can be represented in two dimensions plus time (e.g. geo-spatial, videos, networks, multivariate data). The proper choice of space-time cube operations depends on many factors, for example, density or sparsity of a cube. Hence, we propose a characterization of structures within space-time cubes, which allows us to discuss strengths and limitations of operations. We finally review interactive systems that support multiple operations, allowing a user to customize his view on the data. With this framework, we hope to facilitate the description, criticism and comparison of temporal data visualizations, as well as encourage the exploration of new techniques and systems. This paper is an extension of Bach et al.'s (2014) work

Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Introducing the concept of first and last value to aid lean design: learning from social housing projects In Chile

Value for the customer through efficient production processes is a fundamental principle of Lean. In Lean Construction, Value to customers is largely delivered through project planning and control activities only. Thus, it can be argued that Lean Construction overlooks the opportunity to address Value from the early stages of a project. Aimed at improving this, Lean Design arose as a new approach for design management promoting customer and end user involvement from the early stage of projects. However, even here environmental & social issues are postponed over individual requirements. As a result, Lean potential in general skips the opportunity to address Value from a wider perspective in which the return of Value from the construction industry to society is considered. This paper proposes dividing the wider understanding of the performance of the (global) built environment from the particular (local) project requirements calling the former First Value and the latter Last Value. The theory is triangulated through observation of how a developing country (Chile) is resolving social issues through the use of the built environment. The work described develops Lean Design Management by providing a clearer vision of Value to reduce waste and aid sustainability in the built environment

pyParaOcean: A System for Visual Analysis of Ocean Data

Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes.Comment: 8 pages, EnvirVis202

Intergenerational interpretation of the Internet of Things

This report investigates how different generations within a household interpret individual members’ data generated by the Internet of Things (IoT). Adopting a mixed methods approach, we are interested in interpretations of the IoT by teenagers, their parents and grandparents, and how they understand and interact with the kinds of data that might be generated by IoT devices. The first part of this document is a technical review that outlines the key existing and envisaged technologies that make up the IoT. It explores the definition and scope of the Internet of Things. Hardware, networking, intelligent objects and Human-Computer Interaction implications are all discussed in detail. The second section focuses on the human perspective, looking at psychological and sociological issues relating to the interpretation of information generated by the IoT. Areas such as privacy, data ambiguity, ageism, and confirmation bias are explored. The third section brings both aspects together, examining how technical and social aspects of the IoT interact in four specific application domains: energy monitoring, groceries and shopping, physical gaming, and sharing experiences. This section also presents three household scenarios developed to communicate and explore the complexities of integrating IoT technologies into family life. The final section draws together all the findings and suggests future research